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					Quality Criteria for the Safety Assessment of Cars
                      Based on Real-World Crashes




   Project Summary Report
                                                               CEA/EC SARAC II
                                         QUALITY CRITERIA FOR THE SAFETY ASSESSME

                                           OF CARS BASED ON REAL-WORLD CRASHES

                                                        Funded by the European Commission,

                                                                  Directorate General TREN




            SARAC II
Quality Criteria for the Safety Assessment of Cars
         based on Real-World Crashes

     Contract Number: SUB/B27020B-E3-S07.17321-2002




  Project Summary Report
              Working Group Leaders:

                  Klaus Langwieder

       Comité Européen des Assurances (CEA)

      Brian Fildes                   Max Cameron

     Monash University Accident Research Centre

                      Timo Ernvall

           Helsinki University of Technology




                     March, 2006
                                                                          CEA/EC SARAC II
                                                  QUALITY CRITERIA FOR THE SAFETY ASSESSME

                                                    OF CARS BASED ON REAL-WORLD CRASHES

                                                                   Funded by the European Commission,

                                                                             Directorate General TREN




                      SARAC II
             Project Summary Report
                Co-authors (in alphabetical order)


Name                           Organisation

Judith Charlton                Monash University Accident Research Centre

Anthony Clark                  Monash University Accident Research Centre

Amanda Delaney                 Monash University Accident Research Centre

Liam Fechner                   Monash University Accident Research Centre

Michael Fitzharris             Monash University Accident Research Centre

Heinz Hautzinger               Institute for Applied Transport Research

Timo Kari                      Helsinki University of Technology

Jens-Peter Kreiss              Technical University of Braunschweig

Sjaanie Koppel                 Monash University Accident Research Centre

Anders Kullgren                Folksam Insurance

Astrid Linder                  Monash University Accident Research Centre

David Logan                    Monash University Accident Research Centre

Seppo Mäkitupa                 Helsinki University of Technology

Stuart Newstead                Monash University Accident Research Centre

Ana Olona                      Centro Zaragoza

Esa Räty                       Helsinki University of Technology

Jim Scully                     Monash University Accident Research Centre

Linda Watson                   Monash University Accident Research Centre
                                                                                                    CEA/EC SARAC II
                                                                            QUALITY CRITERIA FOR THE SAFETY ASSESSME

                                                                                 OF CARS BASED ON REAL-WORLD CRASHES

                                                                                             Funded by the European Commission,

                                                                                                       Directorate General TREN




                       International Project Management
                               Comité Européen des Assurances (CEA)
                                        Prof. Dr. Klaus Langwieder

                                            SARAC Members


           European Commission (EC)                                  Comité Européen des Assurances (CEA)
                     DG TREN                                                26 Boulevard Haussmann
                   28 Rue Demot                                                 FR-75009 Paris
                 B-1040 Brussels
                Monash University                                       Helsinki University of Technology
        Accident Research Centre (MUARC)                              Laboratory of Transportation Engineering
                    Building 70,                                                   P.O. Box 2100
          Clayton, 3800 Victoria, Australia                                   FIN-02015 HUT, Finland
BMW Group                              Bundesanstalt für Straßenwesen               Centro Zaragoza
Vehicle Safety                         (BASt)                                       Instituto de Investigación Sobre
D-80788 München                        Brüderstraße 53                              Reparación de Vehiculos, S.A.
                                       D-51427 Bergisch Gladbach                    Carretera Nacional 232, km 273
                                                                                    E-50690 Pedrola (Zaragoza)
DaimlerChrysler AG                     Department for Transport                     FIA Foundation for the Automobile
                                       Zone 1/29a Great Minister House              and Society
D-71059 Sindelfingen                   76 Marsham Street                            8 Place de la Concorde
                                       London, SW1P 4DR United Kingdom              Paris 75008 France

Ministry of Transport and              Finnish Motor Insurers’ Centre               FOLKSAM Insurance Group
Communications of Finland              (VALT)                                       Research/Traffic Safety
P.O. Box 31                            Bulevardi 28,                                S-106 60 Stockholm
FIN 0023 Helsinki                      FIN-00120 Helsinki

Ford Motor Company                     German Insurance Association (GDV)           Honda Motor Europe
Safety Data Analysis (SDA)             German Insurance Institute for Traffic       Wijngaardveld 1
Automotive Safety Office (ASO)         Engineering                                  9300 Aalst Belgium
Köln-Merkenich / Spessartstraße        Friedrichstrasse 191, D-10117 Berlin
D-50725 Köln
Insurance Institute for Highway        ITARDA                                       IVT Heilbronn
Safety (IIHS) &                        Institute for Traffic Accident Research      Institut für Verkehrs- und
Highway Loss Data Institute (HLDI)     and Data Analysis                            Tourismusforschung e. V.
1005 N. Glebe Road                     Kojimachi Tokyu Bldg. 6-6 Kojimachi,         Kreuzäckerstr. 15
Arlington, VA 22201 USA                Chiyoda-ku Tokyo 102-0083 Japan              D-74081 Heilbronn
Japanese Automobile Research           Laboratory of Accidentology,                 Loughborough University
Institute (JARI)                       Biomechanics and Human Behaviour             Vehicle Safety Research Centre
2530 Karima, Tsukuba                   PSA Peugeot-Citroën/RENAULT                  Holywell Building Loughborough
Ibaraki 305-0822, Japan                (LAB)                                        Leicestershire LE 11 3 UZ UK
                                       132 Rue des Suisses
                                       92000 Nanterre (France)
National Organisation for Automotive   Swedish Road Administration (SRA)            Technische Universität
Safety and Victims Aid (NASVA)         Röda Vägen                                   Braunschweig
6-1-25, Kojimachi Chiyoda-Ku,          S-78187 Borlange                             Institut für Mathematische Stochastik
Tokyo, 102-0083, Japan                                                              Pockelsstr. 14
                                                                                    D-38106 Braunschweig
Verband der Automobilindustie (VDA)                                                 Volkswagen AG
Westendstr. 61                                                                      1777 Unfallforschung
D-60325 Frankfurt/Main                                                              D-38436 Wolfsburg
                                                                                           CEA/EC SARAC II
                                                                     QUALITY CRITERIA FOR THE SAFETY ASSESSME

                                                                        OF CARS BASED ON REAL-WORLD CRASHES

                                                                                    Funded by the European Commission,

                                                                                              Directorate General TREN




                                  Document Retrieval Information



Report No.         Date                     Pages
SII-PSR            March, 2006              158

Title and Subtitle
SARAC II Project Summary Report

Author(s)
K. Langwieder, B. Fildes, M. Cameron & T. Ernvall



Performing Organisation
Comité Européen des Assurances (CEA), Paris, France,
 Monash University Accident Research Centre, Victoria Australia,
 Helsinki University of Technology, Helsinki, Finland.

Abstract

This final project report summarises the work of the EU/CEA SARAC II project ‘Quality Criteria for the Safety
Assessment of Cars based on Real-World Crashes’. It is based on the individual subtask reports that have
been established during the project. It summarises the subtask reports and presents recommendations for
future activities and proposals for applications of the now available methodologies for safety rating. This
research report, based on a worldwide review of existing methods and experiences in safety rating, may act
as a basis for further development of safety ratings and should promote the installation of a harmonised
safety rating procedure in Europe.

References in the text will guide the reader to detailed information on the subtasks reports that are annexed
to this final Project Summary Report.

Keywords

SAFETY RATING, CRASHWORTHINESS, ADVANCED DRIVER ASSISTANCE SYSTEMS, REAL WORLD
DATA BANKS, NATIONAL ACCIDENT STATISTICS, SCALING PROCEDURES, VEHICLE OCCUPANT
INJURIES, PEDESTRIAN ACCIDENT ANALYSIS, RISK EXPOSURE DATA, CONSUMER BEHAVIOUR,
CONSUMER SAFETY INFORMATION.

The views expressed are those of the authors and do not necessarily represent those of CEA or any of the
participants of the SARAC committee.
                                                                                             CEA/EC SARAC II
                                                                      QUALITY CRITERIA FOR THE SAFETY ASSESSME

                                                                         OF CARS BASED ON REAL-WORLD CRASHES

                                                                                      Funded by the European Commission,

                                                                                                Directorate General TREN




Acknowledgements
The authors want to thank the European Federation of the Insurance Industry, the Comité Européen des
Assurances (CEA), especially the Motor Management Committee, the Committee ’Prevention and Road
Safety’ and the CEA Secretariat for funding and organising this research project. The authors also
acknowledge the individual colleagues for their excellent cooperation and input of their experience.

The project management would like to thank all involved organisations for their continuous assistance and
for their generosity to support this project with a considerable amount of in-kind contributions to make this
basic research possible.

A special acknowledgement is given to the Monash University Accident Research Centre (MUARC), the
Japanese organisations NASVA, JARI, ITARDA and to the U.S. Insurance Institute for Highway Safety (IIHS)
for extending this EU project to a worldwide project to the benefit of the SARAC research activities, offering
major input and partnership without asking for specific funding.

A special thank you is addressed to the former head of the Transport Unit in DG TREN Mr Dimitrios
Theologitis and his successor Dr Stefan Tostmann, as well as to the project officers John Berry, Willy Maes,
Jean-Paul Repussard and Yves Bosmans.

Major thanks have to be addressed to the excellent organisation of the Project Secretariat in all these years
by Mr Dieter Matthes, to the assistance by Mr Bernhard Greilinger, and to Dr Sjaanie Koppel for formatting
and co-ordinating of this final Project Summary Report, as well as to the Monash University for the
realisation of the editorial work.

Finally, the project management and all project members involved would like to thank the European
Commission, General Directorate Transport and Energy for funding a major part of this SARAC project;
without this grant it would not have been possible to fulfil all the comprehensive tasks aiming at improvement
of road safety today and in the future.
CEA/EC SARAC II                                                                                                                            Table of Contents




Table of Contents – SARAC
EXECUTIVE SUMMARY.............................................................................................................................................. 1
1   INTRODUCTION ................................................................................................................................................ 5
2   THE SARAC COMMITTEE................................................................................................................................ 7
         2.1 SARAC II PROJECT ......................................................................................................................... 7
         2.2 WORKING GROUPS AND SUB-TASKS ................................................................................................. 7
                  2.2.1                Working Group 1 .............................................................................................................................8
                  2.2.2                Working Group 2 .............................................................................................................................9
                  2.2.3                Working Group 3 ...........................................................................................................................10
                  2.2.4                Working Group 4 ...........................................................................................................................11
             2.3 ORGANISATION OF THE WORKING GROUPS AND SUB-TASKS ............................................................ 12
3        CRASHWORTHINESS RATING SYSTEMS ................................................................................................... 13
             3.1 UPDATED AND EXTENDED DESCRIPTION OF EXISTING CAR SAFETY RATING
                   METHODS BASED ON REAL WORLD CRASH DATA (ST 1.2. – 1) ........................................................ 13
             3.2 DESIGN AND ANALYSIS OF MATCHED STUDIES IN EMPIRICAL CAR RESEARCH (ST 1.2—2)................ 22
             3.3 FRAMEWORK FOR ASSESSING OF THE RELATIVE PERFORMANCE OF VARIOUS VEHICLE
                   CRASHWORTHINESS ESTIMATORS THROUGH DATA SIMULATION (ST 3.2) ......................................... 27
4        DATA SOURCES FOR REAL WORLD SAFETY RATINGS.......................................................................... 37
             4.1 IMPROVEMENT OF DATA COLLECTION AND SCALING MEASURES (ST 1.3/4.3) ................................... 37
             4.2 USE OF VIN FOR IDENTIFYING VEHICLE MODEL AND SAFETY EQUIPMENT (ST 1.1.)........................... 39
5        EXPOSURE DATA “PRIMARY SAFETY” AND FLEET EFFECTS ON VEHICLE SAFETY......................... 43
             5.1 ASPECTS OF PRIMARY SAFETY (ST 3.1.)........................................................................................ 43
             5.2 MEASURING THE EFFECT OF PRIMARY SAFETY DEVICES ON ACCIDENT INVOLVEMENT RISK OF
                   PASSENGER CARS – METHODOLOGICAL CONSIDERATIONS .............................................................. 48
             5.3 OCCUPANT AND FLEET EFFECTS (ST 3.3) ....................................................................................... 52
             5.4 IMPROVED SAFETY BY CAR MODEL SERIES (ST 3.3.- 2) .................................................................. 58
6        RELATIONSHIP BETWEEN EURONCAP RESULTS AND REAL WORLD CRASHES............................... 71
             6.1 RELATIONSHIP BETWEEN DRIVER INJURY OUTCOMES AND NCAP RESULTS IN EUROPE AND
                   AUSTRALASIA (ST 2.1/2.2-1).......................................................................................................... 71
             6.2 EXTENDED ANALYSIS OF CAR MODEL SCORES IN GERMAN REAL WORLD DATA (ST 2.1/2.2 – 2) ...... 88
             6.3 ALTERNATIVE WEIGHTING OF NCAP SERIES TO IMPROVE THE RELATIONSHIP TO REAL WORLD
                   CRASHES (ST 2.4.) ........................................................................................................................ 98
             6.4 POSSIBILITIES OF RELATIONSHIP OF NCAP MEASURES AND REAL WORLD INJURIES TO BODY
                   REGIONS (ST 2.3)........................................................................................................................103
             6.5 ANALYSIS OF CAR/PEDESTRIAN CRASH DATA FROM GREAT BRITAIN, GERMANY AND FRANCE
                   (ST 3.4.) .....................................................................................................................................111
7        VALUE OF SAFETY RATINGS FOR CONSUMERS AND POLICY MAKERS ...........................................121
             7.1 STUDIES OF CONSUMER BEHAVIOUR IN SWEDEN AND SPAIN REGARDING CAR SAFETY
                   (ST 4.1.) .....................................................................................................................................121
             7.2 POSSIBILITIES OF ENHANCED CONSUMER INFORMATION (ST 4.2.) ..................................................129
8        RECOMMENDATIONS AND CONCLUSIONS .............................................................................................133
             8.1 RECOMMENDATIONS FOR IMPROVEMENT OF SAFETY RATINGS IN EUROPE ......................................133
             8.2 AREAS REQUIRING FUTURE RESEARCH .........................................................................................139
9        APPENDICES ................................................................................................................................................141
             9.1 APPENDIX 1 DOCUMENT RETRIEVAL INFORMATION FORMS .........................................................15743
             9.2 APPENDIX 2 MEMBERS IN THE SUBTASK WORKING GROUPS ............................................................157
CEA/EC SARAC II                                                                          Executive Summary




Executive Summary
The SARAC II Project aimed to develop advanced methods of Safety Ratings and to apply these methods to
extended accident databases. In addition, the relationship between EuroNCAP results and Real World
Crashes has been investigated and ways in which consumers valued and used safety ratings when
purchasing new vehicles were also explored. A worldwide review of all existing Car Safety Rating methods
was initially conducted, with a detailed description of each method. As all organisations involved in Safety
Rating are members of the International Safety Rating Committee (SARAC), this is an accurate and reliable
summary of these methods.

Theoretical and empirical investigations to improve and assess the relative performance of the various
vehicle crashworthiness estimators have been made. These investigations on a large-scale empirical
material – the German National Accident Statistics - and on simulated data material, have lead to improved
decision criteria regarding which Safety Rating methods are best suited to analyse crashworthiness and
aggressivity, respectively. These studies can be considered to be a guideline for future decisions regarding
how to proceed with Safety Ratings in Europe.

Safety Ratings are dependant on the quality of data sources in real world crashes. In a special study, all data
sources worldwide were analysed and proposals for improved scaling measures developed. The
identification of vehicle models and their safety features needs to be improved. One method would be
through the harmonised application of the Vehicle Identification Number (VIN) by an international body
formed to obtain easier access for research programs.

A detailed system of describing collision types and crash severity needs to be integrated in national police
recorded accident statistics. The worldwide adoption of Event Data Recorder technology (EDR) would also
be useful and criteria for application of this technology are given in this report. Improved scaling measures
for injury severity, not only from in-depth studies but also in large national statistics, are an urgent
requirement. This would facilitate better development of Road Safety Action Programs in Europe and provide
a new level of Road Safety research by Safety Ratings of Cars in real world crashes.

The application of safety ratings in the SARAC II Project has been substantially improved and extended by
use of large-scale national statistics from Great Britain 1993 – 2001, France 1993 – 2001 and, for the first
time in international research, German statistics 1998 – 2002. Comprehensive calculations show the
crashworthiness rating and the injury severity risk of new car models tested by EuroNCAP. The analysis has
been extended to Australia/New Zealand results and also Japanese experiences have been integrated in the
SARAC II Research Reports.

The comparison with NCAP results showed that, on average, design priorities encouraged by EuroNCAP
rating are consistent with a reduced risk of serious injury in cars in real world crashes. A comparison of the
safety development of selected car model series showed in each case that the safety of newer cars is greatly
improved and that in the last decade a reduction of serious injury risk of about 50% has been achieved. The




                                                                                                             1
CEA/EC SARAC II                                                                           Executive Summary


study also showed, however, that the comparison between the prospective EuroNCAP scores and
retrospective SARAC results have only limited correlation, using current databases and procedures.

There is a need in future that the EuroNCAP ratings should be supplemented with results of real world crash
safety ratings to promote a closer link between prospective and retrospective safety ratings and not confuse
consumers. Proposals to do this have been developed in this SARAC Report.

New comprehensive studies of the consumer behaviour were also conducted in Sweden and Spain. They
showed that car safety is a high priority even given the national differences in the different countries, but the
impact of safety on purchasing behaviour still needs to be improved. This is true both for private consumers
as well as for car fleet managers.

Safety related purchasing policies should also be developed and their use promoted by offering incentives.
This new method of consumer studies should be generalised to other European countries especially those
with a poor safety record. A proposal has been developed to use electronic media for improved consumer
information on car safety. It is a critical requirement to improve the information and raise the interest of
consumers in car safety because this is the strongest possibility to integrate, as soon as possible, the
available improvements in-car safety technology in the real world.

Feasibility studies were also conducted in SARAC II in the areas of primary safety and pedestrian accidents
with cars. A review of features of primary safety has been made and real world results of effectiveness of
these systems have been reviewed. ESP has been proven in international studies to be able to reduce the
frequency of accidents with loss of control by 40%. Brake assistance systems (BAS) were also shown to be
highly effective in accident studies. Theoretical considerations regarding how to improve the assessment of
primary safety devices in real world crashes are part of this Research Report.

For the first time in international research, large-scale comparisons of EuroNCAP pedestrian safety ratings
and real world experiences in car to pedestrian accidents have been made. This comparison of safety tests
and real world injury experience showed no evidence of their correlation. Reasons for this may be in the
basic problem of the need to specify single test conditions, selecting only one major crash situation from the
unlimited real world crash situations, limitations with the databases used in these analyses, or the limited
number of NCAP tested cars with upgraded pedestrian protection in the time frame of this report.

Overall, this report covering the whole field of car safety showed that the tools for assessing safety features
and the use of safety rating systems are fully developed and no further research is necessary to assess their
possibilities and limits. The fundamental problem with the development of a high quality rating system for
Europe are the limitations with large-scale national accident statistics, that need to be urgently improved.
Measures such as improved injury severity classifications and recording of impact severity or vehicle
damage would go a long way to addressing this issue. Large in-depth accident databanks in Europe would
also need to be developed and made available for purposes of retrospective safety rating systems.
Proposals outlining how to introduce these changes, without extensive costs, are provided in this report.

The SARAC II research program is now complete. What is now required is a commitment to support
decisions for the application of these basic tools in on-going studies to monitor the quality of car safety in


2
CEA/EC SARAC II                                                                      Executive Summary



general in Europe and to promote the development of further safety features in cars on the road. Several
recommendations are listed towards the end of this report on ways to address these shortcomings.




                                                                                                      3
CEA/EC SARAC II   Executive Summary




4
CEA/EC SARAC II                                                                                    Introduction




1        Introduction
The interest of consumers in the safety of passenger cars and particularly car’s capacity to protect occupants
in the event of an accident has been growing steadily during the last decade. For more than 30 years there
is one predominant question that consumers ask, “What is the safest car?” This question may be answered
either by using prospective or retrospective methods. As real world crash outcomes are the ultimate test for
vehicle safety, SARAC is predominantly concerned with the ongoing need to develop high quality
crashworthiness ratings systems that are based on real world crashes.

In general the safety or the protective performance of cars, also defined as its crashworthiness, has been
part of the motor vehicle safety regulations worldwide.       These regulations describe minimum occupant
protection requirements to standard frontal and side impact crash tests to which all manufacturers of new
cars must adhere. It is a requirement that safety should be more than to fulfil only this minimum level of
safety. The European Commission especially has argued that market pressure and not only stricter
regulation is the most effective way to proceed in safety.

To follow this view of the European Commission, the EuroNCAP program has been developed to assess the
safety of popular models of new cars at the level of crash severity above that which is required by legislation.
Comparable programs have been developed in the United States, Australia and Japan. The NCAP tests
have been substantially extended in the last decade from the basic frontal and side crash tests. Other items
such as pole tests, child restraint assessment, modifiers for potential safety risks, in future tests of head
restraints and also aspects of primary safety have been integrated. Pedestrian protection is now a focus in
car safety testing. EuroNCAP has proven that these prospective tests are the strongest means to improve
car safety within a short time as the consumer is looking onto the star rating of EuroNCAP. Furthermore this
rating is also more and more a subject of manufacturers’ advertising. It is a highly positive effect that after a
certain time a growing number of the major car models are fulfilling the increased safety requirements that
confirm the higher level of safety. But at latest when the majority of all major cars fulfil the increased NCAP
star rating requirements another progress in car safety by new car rating criteria has to follow. This process
over the long term causes a self-dynamic development and it is necessary to continuously receive the
response from real road crash observations.

This may be done by intensified in-depth accident analyses as they are made in the research program of the
European Commission. Another possibility, also more difficult, is to produce an “assessment of car occupant
protection” based on large-scale real world accident data preferably by use of the national accident statistics.
It would be the ideal aim of these so-called       “Safety Ratings of Real World Accidents” to amend and
supplement the results from prospective new car assessment programs. These crash-testing programs
simulate the most likely crash types and they are carried out in controlled laboratory conditions.            An
improvement of these specific tests is assumed to be an improvement of the car’s general increase in safety.
In contrary the safety ratings based on real world accidents integrate all types of crashes and all variations in
drivers, like age, weight and biomechanical characteristics. These real world safety ratings have other




                                                                                                               5
CEA/EC SARAC II                                                                                   Introduction


limitations in that different statistical procedures, which attempt to normalise for the variation in accident
circumstances between different models of cars, may have a significant influence upon rating.

It has been the focal aim of the SARAC II project from which the final report is now being presented to
analyse and optimise the different ratings measures and to get experience in the applications of these
methods to different national statistics in Great Britain, Germany, France, Sweden, and Australia/New
Zealand and to integrate results from Sweden, Japan and USA.

These new research studies of the SARAC II project have continued the analyses of the SARAC I project
which was finished in 2001. SARAC I has been focused on existing Safety Ratings at this time and the
applications have been restricted both by limited sources of large-scale accident analysis statistics and
limitations of the number of EuroNCAP tested cars at this time. As shown above, the SARAC II program
could use the by far extended frame of different European national accident statistics, first time including the
German National Statistics. This allowed intensified applications and comparisons between the different
countries. Furthermore the scope of analysis has been extended. In continuation of the analysis of injury
risks of the driver as in SARAC I, in SARAC II the scope got extended to the items of crashworthiness and
aggressivity, pedestrian protection and first steps into the field of primary safety have been made.

In summary in the SARAC II project the tools for improved safety ratings based on real world accidents are
developed and experiences from extensive applications exist now. In future research the relationship with
EuroNCAP test results has to be continued and the regular application to European national statistics should
complete the existing experiences. However, it should also be mentioned that an improvement of the safety
rating quality requires improvement of the existing European databanks. Proposals of how to do it are given
in this SARAC II report.

The SARAC II Final Report presents all main findings from the SARAC research program on the basis of the
executive summaries of the sub-task reports. For detailed information readers are referred to the specific,
complete sub-task reports provided by the various SARAC sub-task working groups annexed to this SARAC
II Final Summary Report.




6
CEA/EC SARAC II                                                                        The SARAC Committee




2         The SARAC Committee
In 1994 the Institute for Vehicle Safety of the German Insurance Association (GDV) in Munich established
the national advisory group including experts from the accident research community, government agencies,
universities and automobile manufacturers as a discussion forum for all questions regarding vehicle safety
systems based on real world crash data. It soon proved to be necessary to involve safety-rating experts
from all over the world. This group identified and discussed a number of issues of existing safety ratings as
well as proposals of the publication of these ratings. There was a general agreement that more knowledge
and a continuing co-operation between these international experts were necessary and beneficial to improve
car safety. It was therefore decided to establish a Safety Rating Advisory Committee (SARAC) and to
submit an application for a research project to the European Commission.             In 1999 this first research
contract called SARAC I was signed between the EC DG TREN and the European Federation of the
Insurance Industry, the Comité Européen des Assurances (CEA). This first phase of the SARAC research
project has been finalised at the end of 2001. In spite of many highly valid results, SARAC I was limited due
to the available national statistics, their application level, the development level of the existing safety rating
methods at this time and due to the number of comparable EuroNCAP tested cars.


2.1       SARAC II Project
The members of the SARAC I project unanimously voted to submit a proposal for a research project in
“SARAC II”. This project was signed in December 2002 and finished in March 2006. All partners of the
SARAC I project continued to contribute to this project, but in addition, new international partners like
University of Loughborough (SafetyNet, CCIS accident databases), the Japanese National Agency for
Accident Statistics ITARDA and Honda Europe have been integrated in the SARAC Committee.                     The
SARAC Committee thus represents a unique result of co-operation of all organisations worldwide being
involved in safety rating activities based on real world accidents. Due to the close co-operation during long
years of joint research, synergies are existent and co-operation should be continued with the aim to develop
a high quality European Safety Rating System, which may be continuously applied to specific European
National Statistics and EU research databanks. This EU Safety Rating System should also promote the
development of a world standard for the quality of safety rating programs and for a necessary improvement
of databanks. Members of the SARACII committee are listed at the front of this report on Page (iii).


2.2       Working Groups and Sub-tasks
From the SARACI research program further research requirements were identified and specified:

•   Increased understanding of crashworthiness rating methods (car to car and single vehicle crashes);

•   Update of rating methods worldwide;

•   Reasonable measures of safety by using advanced injury scaling criteria;

•   Improvement of data collection and quality, including crash recorder data (EDR) and vehicle



                                                                                                                7
CEA/EC SARAC II                                                                      The SARAC Committee


    identification number (VIN);

•   Effects relating to car occupants, car speeds and car safety features, and

•   Identification of safety rating methods under a theoretical framework combining statistical and physical
    conceptual models of the relationship between injury outcomes, crash circumstances and car model
    parameters.

It has been furthermore suggested to extend SARAC activities to areas that have not been covered in
SARAC I project and to start at least feasibility studies in the following fields:

•   Primary safety (crash avoidance / mitigation) especially regarding advanced driver assistance systems;

•   Pedestrian protection especially analysing the possibilities and limits of field experience with cars
    announcing improved pedestrian protection due to the introduced EC rules;

•   A third field of priority in the SARAC 2 project has been proposed in studies analysing the role of safety
    criteria decisions in the consumers vehicle purchasing decision, and

•   In addition, based on new extensive studies of the consumer’s behaviour in European countries, the
    proposal should be developed how to improve the information of consumers regarding safety criteria of
    cars by the use of new electronic media.

Following the successful structure of the SARAC phase 1, four working groups and in total fourteen sub-task
groups have been defined with SARAC acting as a steering committee and CEA being the main contractor
represented by the Chairman of the CEA Committee “Prevention and Road Safety” Professor Dr.Ing. Klaus
Langwieder. The four working groups and the relevant sub-tasks are described in brief below. A detailed
description is provided in Appendices A and B.


2.2.1     Working Group 1
The main task of Working Group 1 was to describe, analyse and evaluate the existing knowledge of safety
rating methods and to investigate aspects of their practical applications. Basic issues regarding databases,
outcome measures and ratings had to be identified. Possibilities and limits of harmonised rating systems
should be checked.

Sub-Task 1.1 – Vehicle Identification Number

The evaluation of the safety of cars is hampered by the lack of detailed information of safety features
incorporated in crashed vehicles. The use of the vehicle identification number in different countries for a
range of vehicle manufacturers had to be analysed. Ways to use the VIN for identifying car families and
safety features should be developed and proposals for practical application with and without manufacturers
involvement should be made.

Sub-Task 1.2 – Update and Extension of Retrospective Rating Methods

Existing rating methods worldwide should be described under the common conceptual framework –
theoretical considerations for further development of these ratings and possibilities of a harmonisation should



8
CEA/EC SARAC II                                                                        The SARAC Committee



be developed. Proposed methods should be applied in an empirical way to newly available national accident
material.

Sub-Task 1.3 – Scaling Methods and Improvement of Data Collection

This sub-task aims to examine the feasibility of using in-depth data to supplement prospective and
retrospective ratings.    The opportunities for using data from on-board crash recorders (EDR) in
crashworthiness ratings should be analysed. Practical requirements made it necessary to combine the sub-
task 1.3 with the sub-task 4.3 “Concept of SARACDAT”. This combined Sub-Task 1.3/4.3 should define the
entry criteria of accidents for a uniform future European database including similar entry criterion.


2.2.2       Working Group 2
This working group had the central task to correlate and to analyse the relationship of NCAP results and real
world crashes and to extend these studies to the field of car aggressivity and to the assessment of injury risk
in car-pedestrian accidents.

Sub-Task 2.1 / 2.2 – Relationship between EuroNCAP results and real world crashes

This sub-task has used more recent and comprehensive data from U.K. and France as well as from Finland.
After difficult negotiations it was possible to include also a new database, the German National Accident
Statistics covering the five-year period from 1998 to 2002. This German data set could be used for the first
time in an international research program. Furthermore it was possible to use recent national accident data
from the Australian and New Zealand statistics. The integration allowed the pooling of         injury severity of
injured drivers of car models and to gain experiences comparing different national statistics in Europe and
Australia. In addition to continuous support given by the Japanese representatives of NASVA/ITARDA and
JARI, Japanese experiences with the safety rating methods as developed in SARAC II could be integrated.

The relationship of the NCAP results and real world crashes was analysed in total and also in separation into
frontal and side impacts. In addition, these calculation procedures have supported the sub-task 3.4 which
has analysed the possibility of calculating correlations between EuroNCAP pedestrian test results and the
real world outcome of pedestrian injuries in pedestrian to car accidents.

Sub-Task 2.3 – Contribution of NCAP Measures to Correlation Results (body regions)

The investigation within the sub-task needed real crash data from which the injury details (body regions) and
specific measurements of EuroNCAP tested cars can be compared.               The feasibility study showed the
problems to get these results for application in a general accessible European research project and it
showed the problems to receive a sufficient number of accidents with indication of injury to the different body
regions separated into specific car models. In co-operation with a EU project “Safety Net” and by integrating
the Australian experiences of ANCAP versus an in-depth Australian accident material, possibilities and
procedures have been shown how to analyse these questions at present in a limited scope and in future by
extended databanks.




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Sub-Task 2.4 – Comparison of NCAP and real world crashes

The aim of this sub-task has been to study relationships between individual EuroNCAP scores and injury risk
in real world crashes. Separate relationships have to be studied for frontal and side impacts. Possible
differences of EuroNCAP and SARAC results should be analysed:

•    Are the specific EuroNCAP tests correspondent to all crash situations in reality?

•    Is the rating of crashworthiness results adequate?

•    Are the rating procedures of the specific tests in-line with real world accident studies?

Proposals for closer co-operation in future between EuroNCAP and SARAC are made, starting from the
prerequisite that EuroNCAP is the predominant source of safety information and SARAC should contribute to
amend and support the EuroNCAP results.


2.2.3     Working Group 3
Sub-Task 3.1 Exposure data and primary safety

This sub-task had the difficult aim to initiate a first step into the assessment of primary safety effects on car
safety. At first a comprehensive list of available advanced driver assistance systems (ADAS) has been given
and a collection of all existing safety assessments of the ADAS effectiveness based on real world accidents
has been indicated.      The new requirements in promoting further results regarding primary safety are
indicated and examples have been given by use of insurance accident databanks from Finland. The reports
of this sub-task underline the necessity that in large scale national databanks the equipment of cars with
advanced driver assistance systems must be identified for example on the basis of the vehicle identification
number or other adequate measures.

Sub-Task 3.2 Crashworthiness estimation through simulation

The consideration of how observed crash data had been generated from the physical processes via the
development of framework has allowed explicit comparison of what each rating system actually represents.
This sub-task defined the need for and the adequacy of adjustment processes to control the effects of factors
besides vehicle model on occupant injury risk. An empirical benchmark method has been used to study the
relative performance of each safety rating method using simulation crash data. This new developed
procedure quantifies the ability of each rating method to accurately estimate the known vehicle safety
performance (which was the basis for creating the simulated crash data).

Sub-Task 3.3 Car occupant and fleet effects

In this sub-task it has been investigated in more detail how the car fleet itself and the changes in the fleet
influence the risk rate based on data from both car to car and single vehicle accidents. Furthermore car
occupant effects, their behaviour and the use of the car in different urban/rural districts (influencing the
accident involvement of specific car models) have been analysed. Results showed that recent car models
offered much more safety, compared to the predecessor model of the same car. Thus, the developed safety
rating models within the SARAC II project have shown that they are able to identify the improved process in
safety over the years.

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CEA/EC SARAC II                                                                        The SARAC Committee



Sub-Task 3.4 Pedestrian accidents

First time in international studies it was possible to analyse specific pedestrian accident material and to
compare the real world crash results with the prospective rating by EuroNCAP. Experiences in the limited
availability of model related car-pedestrian accident material even in large-scale national statistics are given.
Relationship between EuroNCAP star rating and real world injury observations could not be observed. This
might be due to the limited number of high star rewarded cars in EuroNCAP tests or with other injury causes
related to different collision types in real world pedestrian accidents.


2.2.4     Working Group 4
Sub-Task 4.1 Understanding safety rating results by consumers

Little detailed knowledge has been published about the role that safety plays in vehicle purchase decisions.
A comprehensive survey was undertaken in two European countries (Sweden and Spain) that have differing
safety records to examine car-purchasing decisions.            The study showed detailed extensive results
concerning the car purchasing criteria of private clients and fleet managers. This offers possibilities by
national authorities to promote the implementation of advanced safety technologies by incentives.

Sub-Task 4.2 Safety ratings and enhanced consumer information

This subtask set out to explore the concordance of safety ratings across different systems and ways of
promoting safety among vehicle consumers. The results shows reasonable concordance between two
European real world rating systems with EuroNCAP and develop a model of a web-based system capable of
informing consumers about safety features and benefits which also provided a method of assisting
consumers in choosing a vehicle that met their purchasing criteria. A proposal is presented how to develop
optimised consumer information showing the available safety features and giving advice regarding their
effectiveness.

Sub-Task 4.3 Concept for SARACDAT

This sub-task has been integrated in the sub-task 1.3 “scaling measures and improvement of data
collection”. Examples for the possibilities and limitations of the use of in-depth data sources are indicated in
this combined 1.3/4.3 sub-task. The necessity of integrated European databanks is clear.

Sub-Task 4.4 Proposals and requirements for safety improvement and for legislative actions

A comprehensive list of recommendations has been developed, addressing research units, national
authorities (especially national and statistical offices) and governmental departments.          There are still
comprehensive improvements of national databanks possible and necessary and a higher quality of safety
information for future traffic safety improvement is strongly required. Details of the Worksheets describing
each of these Sub-Tasks and their participants are shown in Appendix 1 of this report.




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2.3        Organisation of the Working Groups and Sub-Tasks
A working group leader has guided every working group. Within the working group there has been a set of
sub-tasks, each guided by their pilot.     In the worksheets, which are proved in the Appendices, the
participants of the sub-tasks are listed. Special tasks and responsibilities have been defined for the three
categories of sub-task participants namely the sub-contractors, the advisors and the observers. With this
systematic it was possible to use both the contributions of the sub-contractors and the advisors/observers
that could support the SARAC project by their special experience without being in the formal responsibility of
a sub-contractor. The contribution of the advisors/observers gave major support to the SARAC II research
reports and their results as well as crash data without burdening the SARAC II research budget.

Special working group meeting were held in conjunction with SARAC Main Assembly Meetings.                This
provided the opportunity for all participants to review the progress to date, to identify and discuss any
outstanding or difficult issues, to recruit any additional assistance required and to review the research
program.

Papers were presented at international conferences on some of the more principal issues and results. A
comprehensive document list has been developed which shows both the continuous process of drafting and
finalising the sub-task reports as well as documenting all intermediate papers and contributions to integrate
international experience in the progress of the SARAC II project.

A summary report was prepared by each working group/sub-task leader on the outcomes of their group
efforts as listed in the following chapters. These reports are listed in the document retrieval information
sheets directly annexed to this SARAC II Final Summary Report. In addition every sub-task report is printed
separately and integrated in the CD of the SARAC II Final Summary Report.




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3         Crashworthiness Rating Systems
3.1       Updated and extended Description of existing Car Safety Rating
          Methods based on Real World Crash Data (ST 1.2. – 1)
Nine different car safety-rating methods were described in detail and discussed regarding the theoretical
concept behind each method. The rating methods considered here were developed by the following
institutions:

•   Folksam Research (Sweden);

•   University of Oulu (Finland);

•   Monash University (Australia);

•   Department of Transport (UK);

•   Insurance Institute for Highway Safety (USA);

•   Highway Loss Data Institute (USA);

•   AFO Institute (Germany), and

•   Japan Automobile Research Institute (Japan).

While most of the alternative methods are based on police data, insurance data are used as the main source
in Finland and the US. In Sweden and Australia, police data are matched with insurance claims. Safety
rating methods based on crash test data (i.e. experimental approaches) have not been studied here. The
report is an updated and extended version of an earlier report (SARAC1, Sub-Task 1.1).

The main characteristics of the car safety rating methods considered here are summarised in the last
paragraph of this summary. More details, particularly about the underlying indicators of primary and
secondary safety, are to be found in the full report. As a prerequisite for standardized description of the
alternative approaches a general conceptual framework for measuring car safety was developed which now
is presented first.

Part A:      Conceptual Framework

Car safety concepts

Car safety is a theoretical concept that can be specified in different ways. One can at least distinguish the
following safety concepts:

•   Primary or active safety: ability to avoid crashes

•   Secondary or passive safety: ability to avoid injury sustained in a crash

Secondary car safety may further be looked at under different perspectives:

•   Vehicle crashworthiness: ability of a vehicle to protect its own occupants in collisions




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•    Vehicle aggressivity: degree to which injury is inflicted upon occupants of the vehicle or road user with
     which the "subject" car crashes

•    Vehicle total safety: combination of vehicle aggressivity and crashworthiness

All descriptions and considerations of this report focus on crashworthiness rating.

Both primary and secondary safety of cars can be measured by appropriate risk quantities:

•    Accident involvement risk is the standard indicator of primary car safety.

•    Occupant injury risk (given accident involvement) serves as a measure of vehicle crashworthiness.

Risk factor car make and model

Not surprisingly, a variety of occupant injury risk concepts exists. The use of a specific injury risk quantity is
frequently dictated by the nature of the accident database available.

Of course, "car model" or "car make and model" is the risk factor (determinant of occupant injury) of primary
interest. One may consider "car model" under two alternative perspectives:

•    Car model as a complex attribute summarising all physical and design properties of a vehicle.

•    Car model as an attribute characterising only the car's design properties and secondary safety fittings.
     (The car's physical properties like mass are in this case considered as a different aspect).

If car mass and car model are assumed to represent different aspects of a vehicle, occupant injury risk may
be adjusted for mass effects in order to measure the pure effect of the car's structure, design and secondary
safety fittings on occupant protection.

Measures of car occupant injury

Different scales are used to measure the criterion variable "occupant injury". As a clinical definition of
occupant injury is typically not available, certain validity problems arise in car safety ratings. Sometimes only
the crude binary attribute "injured: yes/no" is available. Injury information may not necessarily be available for
all occupants of accident-involved cars. Thus, crashworthiness of a car is measured typically by driver injury
risk only.

Investigations in secondary car safety may be viewed as special cases of epidemiological studies as they
deal with the distribution and determinants of a specific "disease" (injury due to accident involvement) in
specific human populations (accident-involved car drivers). Both descriptive and analytical (or aetiological)
approaches may be useful in car safety rating. Descriptive analyses where the average safety level of
different car models is estimated are valuable, for instance, from the vehicle insurance point of view.
Analytical studies designed to measure the partial effect of car model on crashworthiness preferably
correspond to the consumer's perspective.

Concept for measuring the chance of car occupant injury

In car passive safety (crashworthiness) rating the universe of accident-involved cars or more precisely the
universe of accident involvements of cars can be considered as the "population at risk". Of course, this


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population has to be defined with respect to factual, spatial, and temporal characteristics. The elements of
the population at risk, i.e. the study units, are "events" (accident involvements of cars) occurring in time and
space.

For each car accident involvement, information on the following variables is needed:

•   Injury status of car occupants (criterion variable)

•   Make and model of accident-involved car (risk factor to be assessed)

•   Other factors which might affect the criterion variable (concomitant variables).

In crashworthiness rating one is interested in evaluating the chance that the occupants of a car of a certain
make and model are injured in case of an accident. An appropriate basic measure is the probability of the
car driver being injured given that the car belongs to a particular category of make and model. This is called
the risk of car driver injury.

In our context risk describes the relationship between car model and car driver injury. This, however, is not
sufficient for assessing the risk factor to injury outcome. As in other fields of evaluation research a
comparison group is required which, for instance, may be the group of accident-involved cars that do not
belong to the car model category under consideration (group without the risk factor). This leads to the
definition of relative risk as the ratio of injury risk for the drivers of a particular car model to the injury risk for
the drivers belonging to the comparison group.

However, risk as a probability is not the only possibility of specifying "chance". An alternative specification is
called the odds. The odds measure the number of times accidental injury occurs relative to the number of
times it does not. The odds can be calculated for different groups. In car safety rating one is interested in the
ratio of the odds for a particular car model to the odds of a comparison group that, for instance, may consist
of all other car models. The corresponding measure is called odds ratio.

It is important to note that in epidemiology and other fields of applied statistics researchers take 'odds' and
'odds ratio' to refer to the chance of disease (accidental injury) incidence just as they do 'risk' and 'relative
risk'. In practice, the odds as such are rarely of interest, and the odds ratio is generally quoted alone. In
passive car safety rating several alternative ways to measure crashworthiness are possible and suitable.
There is no single or most adequate index of injury risk up to now. However, careful interpretation of the risk
indicators used is necessary.

Matched pairs designs

The different concepts of specifying 'chance' are characterised more detailed in the full report. Several rating
methods described in this report restrict themselves to quantify car crashworthiness solely on the basis of
analysing two-car accidents. Therefore, special attention must be paid to the so-called 'matched pairs
design'.

Typically, the restriction to two-car accidents is to minimise distortion which would be caused to the
estimates of driver injury risk if, for instance, a particular car model had a high proportion of collisions with



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much larger vehicles such as Iorries or busses. From a statistical point of view restriction to two-car
accidents corresponds to a so-called "matched pairs design" (also termed 1:1 matching or pair matching)
which has the advantage of high internal validity since all observed and unobserved characteristics of the
accident itself (time, Iocation, weather conditions etc.) are the same for both accident-involved cars and,
therefore, these characteristics cannot account for possible differences in the injury risk of the two drivers
involved in the accident. Consequently, "confounding" is avoided and the "pure" effect of car model on the
chance of car driver injury can be measured more precisely. External validity, however, is reduced since a
substantial part of the population of all accidents (single car crashes, crashes against freight transport
vehicles etc.) is ruled out. This is an obvious weakness of the matched pairs design.

Summarising it can be said that when adjustment for confounding is made at the design stage of the study
by choosing the concept of matched pairs this must be taken into account in the stage of data analysis. A
matched pairs study requires a matched pairs analysis, which can be more complex both to understand and
compute.

Adjustment for confounding factors

When comparing two different car models with respect to driver injury risk (given accident involvement) the
problem may arise that the two driver populations differ significantly as far as driver age is concerned. Since
driver injury risk ceteris paribus depends on driver age a direct comparison of risk rates related to different
car models may be misleading. Consequently, in any analytical approach to car safety rating adjustments of
group-specific risk rates are necessary to remove the confounding effect of concomitant variables.

In epidemiology both direct and indirect methods of adjustment for categorical concomitant variables are
used. These methods are based on the concept of a "standard population":

When the direct method is applied, the adjusted injury risk for certain subgroups of drivers is calculated
under the assumption that the subgroups had the same structure (e.g. driver age distribution) as the
standard population that has to be specified prior to the group comparisons. The direct method is especially
suitable when many groups (car models) are to be directly compared. In the case of adjusting for driver age
the indirect method would mean to calculate the adjusted injury risk for each subpopulation (car model)
under the assumption that the different age groups in the subpopulation had the same risk rates as the
corresponding age groups in the standard population. Consequently, the indirect method is suitable when a
specific subgroup is to be compared with the population. An indirectly standardized risk rate is often called
expected risk rate. In epidemiology the ratio between "observed" and "expected" risk rates is usually termed
"critical ratio" or "comparative injury index". The University of Oulu rating system uses the indirect method
(adjustment for age and gender), for instance.

Adjustment for metric concomitant variables like car mass usually is based on certain regression models. As
a result one obtains, for instance, mass-adjusted injury risk quantities. In epidemiology one speaks of
"attributable risk" (attributable to risk factor car model). This concept may also be used to adjust occupant
injury risk simultaneously for several concomitant variables. The methodologies of DETR and Monash
University are built on such an adjustment procedure.



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Part B:      Standardised Description of Existing Car Safety Rating Methods

In view of the conceptual framework summarized above, the nine alternative car safety-rating methods have
been described according to the following scheme:

•   database

•   methodological background

•   population at risk

•   car safety indicators (risk quantities)

•   adjustment method

•   rating procedure.

Despite the great diversity of the rating methods under consideration this standardised format proves to be
applicable and useful.

The main characteristics of the car safety rating methods under consideration are documented in the Tables
1 and 2 below.




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Table 1           Standardised description of existing car safety rating methods (Part I)
                                                                               General nature of population at
 Publishing                Car safety aspects
                                                General research design        risk
 Organization              covered
                                                                               (exposure quantity)
 Folksam Research          Secondary safety     Retrospective research         Accident involvements of cars
 (Sweden)                  - crashworthiness    design based on the            i two-car accidents
                                                matched pairs concept          i accidents where at
                                                                                   least one driver or front
                                                                                   seat passenger was
                                                                                   injured
 University of Oulu        Secondary safety     Retrospective research         Accident involvements of cars
 (Finland)                 icrashworthiness     design based on the            i two-car accidents
                           iaggressivity        matched pairs concept
                           itotal safety
                           primary safety
                           iinvolvement in
                             injury accident
 Monash University 1       Secondary safety     Retrospective research         Accident involvements of cars
 (Australia)               icrashworthiness     design
                           iaggressivity
 Department for            Secondary safety     Retrospective research         Accident involvements of cars
 Transport, Local          icrashworthiness     design (although only two-     i two-car accidents
 Governments and           iaggressivity        car accidents are              i injury accidents
 the Regions (UK)          primary safety       considered data analysis is
 (DTLR formerly            iinvolvement in      not based on the matched
 DETR)                       injury accident    pairs concept)
 Insurance Institute       Primary and          Retrospective research         "Vehicle years" of
 for Highway               secondary aspects    design                         registered vehicles (cars)
 Safety (USA)              of safety
                           iinvolvement in
                             accident with
                             death to driver
 Highway Loss              Primary and          Retrospective research         "Vehicle years" of
 Data Institute            secondary aspects    design                         insured vehicles (cars)
 (USA)                     of safety
                           iinvolvement in
                             accident with
                             injury to car
                             occupant
 AFO Institute             Secondary safety     Retrospective research         The concept of population at
 (Germany)                 icrashworthiness     design (although only two-     risk does not apply here. Study
                                                car accidents are              units are accident
                                                considered data analysis is    involvements of cars meeting
                                                not based on the matched       the following criteria
                                                pairs concept)                 i two-car accident
                                                                               i car driver not guilty
 Japan Automobile          Secondary safety     Retrospective research         Accident involvements of cars
 Research Institute        i crashworthiness    design (although only two-     i two-car accidents where
 (Japan)                                        car accidents are                  a JNCAP tested vehicle
                                                considered, the study is not       (=subject car) was involved
                                                based on a matched pairs       i accidents where at least
                                                design)                            one driver was injured
 Monash University 2:      Secondary safety     Retrospective research         Accident involvements of cars
 Newstead Method           i crashworthiness    design based on the            i two-car accidents
 (Australia)                                    concept of conditional risk    i driver of opponent car
                                                                                   must be injured




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Table 2           Standardised description of existing car safety rating methods (Part II)
 Publishing          Indicators of car        Indicators used for         Adjustment of safety       Grouping of car
 Organization        safety considered        car model safety            indicators                 models when
                                              rating                                                 publishing
                                                                                                     rating results
 Folksam             Crashworthiness          Safety rating of car        Relative injury risk of    Car models are
 Research            indicators               models is based on the      drivers of the "subject"   grouped by four
 (Sweden)            i relative injury risk   ratio of                    car model is adjusted      different
                     of      car driver (as   i relative serious          for                        categories
                        compared to             injury risk for subject   i mass of subject car      according to size
                     injury risk of driver      car model                    model (heuristic        of car.
                     of      opponent         and                            procedure)
                     car) given               i mean value of             i accident year
                        accident                relative serious             (regression
                        involvement             injury risk over all         approach)
                     i Conditional risk         car models                Relative driver injury
                     of      serious          Depending on this ratio     risk is multiplied by an
                     accident                 each car model is           additional correction
                        consequences          assigned to one of five     factor. After this
                        (death or             (ordered) safety            correction the risk rate
                     permanent                categories.                 is interpreted as
                        disability) for                                   relative injury risk of
                     injured          car                                 drivers and front seat
                     occupants                                            passengers.
                     i product of the
                     two risk
                     quantities
                        mentioned
                     above,           i.e.
                     relative risk of
                        serious injury
                     given accident
                        involvement
                     (relative
                        serious injury
                     risk)
 University of       Crashworthiness          Safety rating of car        Expected numbers of        Car models are
 Oulu (Finland)      indicator                models with respect to      driver injuries are        grouped by five
                     i absolute driver        crashworthiness,            determined for six         different mass
                     injury risk for          aggressivity and total      different groups of        categories.
                     subject car              safety is based on the      drivers (by age and
                     Aggressivity             ratio of                    sex) by using an
                     indicator                i actual number of          elementary indirect
                     i absolute driver           driver injuries for      standardization
                     injury risk for             subject car model        method.
                     opponent of              and                         After summation of
                        subject car           i expected number of        expected numbers
                     Total safety                driver injuries for      over the groups an
                     indicator                   subject car model        additional adjustment
                     i sum of the two         i.e. on a relative injury   is made for
                        indicators            risk quantity. This risk    i speed limit of road
                     mentioned                quantity, however,          i guilt of driver
                        above                 does not make use of        i accident type
                     An additional            the matched pairs           i injury severity.
                     crashworthiness          design. Based on the




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                  indicator is defined     calculated ratio each
                  as the ratio of the      car model is assigned
                  above                    to one of three
                  crashworthiness          (ordered) safety
                  and total safety         categories.
                  indicators. As such
                  it is a relative risk
                  measure for the
                  matched pairs
                  design.
 Monash           Crashworthiness          Safety rating of car         Car model specific        Car models are
 University 1     indicators               models is based on           crashworthiness           grouped by eight
 (Australia)      i absolute driver        comparisons of car           indicators are            different "market
                  injury risk for          model specific               calculated using a        groups" related
                  subject car              crashworthiness              logistic regression       to mass, size and
                      model                indicators. Rank order       model. By this            cost.
                  i conditional risk of    of car models is             standardization
                      serious injury for   determined according         approach adjustment
                      injured driver       to the corresponding         is made for
                  i product of the         point estimates of the       i driver gender
                  two risk                 crashworthiness              i driver age
                  quantities               indicator.                   i speed limit at crash
                      mentioned                                            location
                  above,            as                                  i number of vehicles
                  the final indicator                                      involved in crash
                                                                        i state of crash
                                                                           location
                                                                        i year of crash
 Department of    Crashworthiness          Safety rating is based       Corrected driver injury   Car models are
 Transport (UK)   indicator                on "corrected" driver        risk for car models is    grouped by four
                  i absolute injury        injury risk differentiated   calculated using a        different
                    risk for driver of     by four different size       logistic regression       categories
                    subject car model      groups of cars.              model. By this            according to size
                                           Depending on the car         standardization           of car.
                                           model specific injury        approach adjustment
                                           risk as compared to          is made for
                                           the mean injury risk of      i speed limit of road
                                           the corresponding            i first point of impact
                                           group, each car model        i driver gender
                                           is assigned to one of        i driver age.
                                           three (ordered) car
                                           safety categories.
 Insurance        Combined indicator       Safety rating is based       None                      Car models are
 Institute for    of primary and           on the relative driver                                 grouped by
 Highway          secondary safety         death rate which is the                                seven body style
 Safety (USA)     i absolute risk of       ratio of                                               categories and
                     involvement in        i driver death rate for                                within each body
                     accident with            subject car model                                   style group
                  death to driver of       and                                                    according to
                  subject         car      i average driver death                                 three vehicle size
                  model ("driver              rate for all cars.                                  groups.
                     death rate")          In addition, relative                                  Within each
                                           driver death rates are                                 group car models
                                           computed for                                           are ranked
                                           subgroups of cars                                      according to the
                                           defined by body style                                  model-specific
                                           and vehicle size.                                      relative driver
                                                                                                  death rate. In
                                                                                                  addition, each

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                                                                                                    car model is
                                                                                                    assigned to one
                                                                                                    of five (ordered)
                                                                                                    car safety
                                                                                                    categories.
 Highway Loss     Combined indicator        Safety rating is based       Injury claim rates for     Same as for
 Data Institute   of primary and            on the mean relative         car models are             Insurance
 (USA)            secondary safety          standardized injury          standardized by            Institute for
                  i absolute risk of        claim rate which is the      operator age group.        Highway Safety
                     personal injury        ratio of                     After adjustment
                  for    occupants of       i standardized injury        model-specific claim
                  subject        car          claim rate for subject     rates refer to the same
                  model ("injury claim        car model                  distribution of operator
                  rate")                    and                          age group for all car
                                            i overall standardized       models.
                                              injury claim rate.
 AFO Institute    Secondary safety          Safety rating is based       Mean monetary injury       Car models are
 (Germany)        indicator                 on mean monetary             cost per accident-         grouped by six
                  i mean monetary           injury cost for subject      involved car is            vehicle weight
                    injury cost per         car model as                 standardized by car        classes.
                    accident-involved       compared to mean             occupancy. After
                    car                     monetary injury cost         standardization model-
                                            for all car models of        specific mean injury
                                            the corresponding            costs refer to the same
                                            vehicle weight class.        car occupancy rate for
                                            Depending on this ratio      all car models.
                                            each car model is
                                            assigned to one of six
                                            (ordered) safety
                                            categories.
 Japan            Crashworthiness           Safety rating is not the     Adjustment is made for     Does not apply
 Automobile       indicator                 study purpose. Rather,       more than 10
 Research         i absolute driver         the validity of the          covariates describing
 Institute        injury risk for           JNCAP test procedure         the accident, the
 (Japan)          subject car               is checked. Thus, the        subject car and its
                                            number of JNCAP              driver, and the
                                            stars is the “risk factor”   opponent car (vehicle
                                            to be analyzed.              mass).
 Monash           Crashworthiness           See Monash                   See Monash                 See Monash
 University 2:    indicator                 University 1                 University 1.              University 1
 Newstead         i absolute driver                                      Instead of “number of
 Method              injury risk given                                   vehicles involved in
 (Australia)      that the driver of                                     crash” the covariate
                  the opponent car                                       “point of impact” is
                  is      injured                                        used.
                  i   conditional risk of
                      serious injury for
                      injured driver
                  i product of the
                  two risk
                  quantities
                    mentioned above




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3.2         Design and Analysis of Matched Studies in Empirical Car
            Research (ST 1.2—2).
Crashworthiness as the ability of a vehicle to avoid injury to its own occupants in collisions is an important
research subject in the field of traffic safety. According to the general definition of crashworthiness, some
measure of car driver injury risk (given accident involvement of the car) is normally used as an indicator.
Among the many determinants of car driver injury status, the risk factor “car make and model” is of special
interest. In car safety rating it is even the central risk factor to be assessed.

Clearly, crashworthiness studies can be designed in several different ways. In this report only survey or ex-
post-facto approaches have been considered. This means that crashworthiness indicators are calculated on
the basis of empirical accident data. Since the conditions under which the process of data collection takes
place cannot be set in advance as in an experiment, the data have to be structured after collection to control
for the effects of variables other than car model (so-called confounders). In this context matching proves to
be a powerful concept.

Matching as a study design concept in car safety research

When risk factors, i.e. determinants of car driver injury status, are to be assessed “matching” simply means
that the cars involved in the same two-car accident are considered as a single “matched pair” rather than two
independent observations. By matching one automatically adjusts car driver injury risk measures for
confounding accident-specific variables (including unobserved and unobservable confounders). If the
matched-pairs data are analysed using an appropriate regression model of driver injury one can, in addition,
adjust for car- and driver-specific variables.

When adjustment is made at the design (or data preparation) stage of the study by the concept of matched
pairs this must explicitly be taken into account in the stage of data analysis. A matched-pairs study requires
a matched-pairs analysis, which can be more complex both to understand and compute (Woodward, 1999,
p.267ff), but also more effective than approaches lacking appropriate methodological rigor. In order to
choose the suitable statistical analysis methods one must answer the following questions:

•    Is the assessment of the risk factor under consideration to be made without or with adjustment for
     confounding car- and driver-specific1 variables?

•    Is driver injury status a binary variable or is it measured on an ordinal scale?

•    Are we mainly interested in testing association between risk factor and driver injury?

•    Is estimating comparative chance of driver injury a main concern of the study?

Depending on the approach to be chosen and the nature of the data available, the statistical toolbox offers
various methods. The candidate methods can be broadly classified into models with population-averaged
and models with accident-specific parameters. The two approaches differ in the way of modelling the
dependence between the injury status of the two car drivers belonging to the same accident. The first



1
     Adjustment for accident-specific covariates is automatically made due to matching.
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CEA/EC SARAC II                                                            Crashworthiness Rating Systems



approach leads to log-linear models of driver injury in two-car accidents, the second to so-called fixed effects
models of driver injury for cars matched in pairs. In the statistical sciences fixed effects models are the
preferred tool for analysing matched-pairs data, e.g. data from matched case-control or matched cohort
studies. It turned out that this is also valid for crashworthiness studies based on two-car accident data.

Assessment of risk factors without adjustment for car- and driver specific variables

In this case the data to be analysed can be displayed in a two-dimensional contingency table. When both
injury status and car make and model are binary variables a 2x2 table will arise. For the construction of this
table two possibilities exist:

•   Design I: cross tabulation of accidents by injury status of subject car driver and other car driver
    (corresponding to a matched cohort study design)

•   Design II: cross tabulation of accidents by car make and model of injured driver and uninjured driver
    (corresponding to a matched case-control study design).

The null hypothesis of no association between the risk factor “car make and model” and car driver injury
status can be tested using a so-called symmetry test. When dealing with 2x2 tables McNemar’s test can be
used. When there are more than two levels of driver injury (matched cohort design) or more than two
categories of car models (matched case-control design) the empirical accident frequency data will be
displayed in r x r tables. For r x r tables Bouwker’s test is the appropriate method for testing the hypothesis
of no association.

For matched studies the matched odds ratio is the appropriate measure of comparative chance of driver
injury. The matched odds ratio can be estimated from the 2x2 table; it appears that only the off-diagonal
elements of the table are relevant for point estimation. When under a matched case-control study design
where several car models are to be distinguished the empirical frequency data are displayed in a r x r table,
the odds ratio for all pairs of car models can be calculated. Confidence intervals for the population value of
the matched odds ratio can be computed using the F-distribution.

Assessment of risk factors with adjustment for car- and driver specific variables

When the adjusted odds ratio for the risk factor car make and model is of interest (adjustment for
confounding car- and driver-specific variables), the statistical analysis of two-dimensional contingency tables
is no longer sufficient. Rather, specific regression models for car driver injury status are needed which in
addition to the risk factor also contain confounding factors as explanatory variables. As injury status of the
drivers involved in the same accident can never be regarded as two independent observations, the cluster or
multilevel structure of the data (level 1: accidents; level 2: accident-involved cars) must be taken into
account.

Among several alternative statistical models the fixed effects logit model appears to be most suitable for the
analysis of matched-pairs accident data, especially when both theoretical and practical considerations play
an important role. In order to obtain empirical estimates of the regression parameters and the corresponding
(adjusted) odds ratios one can transform the fixed effects logit model in a specific way (“conditioning out



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CEA/EC SARAC II                                                              Crashworthiness Rating Systems


accident-specific fixed effects”) leading to the so-called conditional logistic regression model for matched-
pairs data. This model can be estimated using standard logistic regression software. Very briefly, the method
for estimating the parameters can be described as follows:

•    Eliminate all accidents where injury status of the two drivers does not differ.

•    Create difference scores for all car- and driver-specific covariates (variable value for car 1 minus variable
     value for car 2)

•    Use maximum likelihood to estimate the logistic regression predicting injury status of driver of car 1 with
     the difference scores as predictor variables.

Finally, it should be noted that from matched-pairs data we cannot estimate the absolute risk of driver injury
but only the comparative chance of driver injury. This, however, is sufficient in many situations, especially for
studies aiming at a ranking of various car models with respect to crashworthiness.

Selected empirical results

The various statistical methods described above have been successfully applied using data on            n=490'813
two-car accidents from the GerDAT database (German Traffic Accident Statistics 1998 to 2002). Numerous
comparisons have been made between cars of different make and model using different definitions of driver
injury and assessing the car’s crashworthiness both with and without adjustment for confounding variables.

If, for instance, a comparison is made between Golf-3 and all other car models an estimated odds ratio of
0.791 is obtained indicating that for Golf-3 drivers the chance of severe or fatal injury is 21 percent lower.
According to McNemar’s test the superiority of Golf-3 is highly significant (chi-square distributed test statistic

SN=38.5897,      1 df,   p-value    lower than 0.0001). These results were obtained from       d=2'822    crashes

(“discordant pairs”) where exactly one Golf-3 was involved and where injury status of the two drivers was
different.

Table 3           Crash Outcomes of drivers involved in collisions with Golf-3

     Accidents                                                   Other car model driver

                                                Not injured             Injured                Total

     Golf – 3 driver      Not injured                    46534                     1576                48110

                          Injured                        1246                          654             1900



                          Total                          47780                     2230                50010



                                                McNemar's Test
                            Statistic (S)      38.5897
                            DF                       1        Odds Ratio      0.7906
                            Pr > S              <.0001


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The GerDAT database contains d=125 crashes between Golf-3 and Golf-2, where injury outcome is different

for the two drivers. As in only 48 of these cases the Golf-3 driver was the injured driver the matched odds
ratio is estimated at 0.623 (=48/77). Using McNemar’s test the hypothesis of equal crashworthiness can be

rejected at the 1 percent level (SN=6.7280, 1 df,     p-value 0.0095). Due to the small sample of discordant
matched pairs the approximate 95 percent confidence interval for the population value of the odds ratio is
quite broad and reaches from 0.43 to 0.88. Obviously, in this situation the comparative chance of Golf-3
driver injury can only be estimated with relatively low precision.

When driver injury status is an ordinal variable with several levels the hypothesis of equal crashworthiness
can be tested under a matched cohort study design using Bowker’s test of symmetry. In the GerDAT
database driver injury status is described by an ordinal variable with 4 levels (uninjured, slightly injured,
severely injured, killed). In a comparison between Golf-3 and all other car models the symmetry hypothesis
of equal crashworthiness can be rejected at the 0.01 percent level (chi-square distributed test statistic

SB=57.8265, 6 df, p-value lower than 0.0001).
Under the matched case-control design a safety comparison has been made between several subgroups of

cars. As   m=8 subgroups of cars (different make and model) have been distinguished, in total m(m-1)/2=28
matched odds ratios have been estimated from the corresponding 8x8 contingency table. Not surprisingly,
Bowker’s test leads to the rejection of the null hypothesis of identical crashworthiness of the car models
under consideration.

Table 4             Safety comparisons between selected vehicle models

Accidents                                            Car model of uninjured driver
                        Astra-1 Escort-4 Fiesta-3     Golf-2    Golf-3 Passat-3      Polo-2   Others     Total
Car      Astra-1           191       116        84      269          342   154          83     5382     6621
model of
injured Escort-4           128        98        70      167          188    108         49     3568     4376
driver   Fiesta-3          186       130        96      256          285   173          88     5168     6382
           Golf-2          320       222       146      479          538   264         168     9451     11588
           Golf-3          316       208       146      433          476   238         130     8726     10673
           Passat-3         95        67        50      174          202    106         69     3464     4227
           Polo-2          171       104        75      237          327   163          98     4977     6152
           Others         5519      3560      2576     7324      8795      4309       2567 115382      150032
           Total          6926      4505      3243     9339    11153       5515       3252 156118      200051
                                    Bowker's Test of Symmetry
                                    Statistic (S)      2457.5856
                                    DF                         28
                                    Pr > S                 <.0001




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CEA/EC SARAC II                                                            Crashworthiness Rating Systems


The fixed effects logit model has also been applied to empirical two-car accident data from the German
traffic accident statistics. Driver injury status has been treated as a binary variable defined as “severely
injured or killed yes/no”. To keep the example simple, the risk factor car make and model was also
considered to be a binary variable defined as “Golf-3 yes/no”. The odds ratio for the risk factor has been
estimated both without and with adjustment for confounding factors using the LOGISTIC procedure of the
SAS system. To illustrate the application of the fixed effects logit model a vehicle characteristic (car mass in
kg) and a driver characteristic (driver gender) have been selected as covariates from a larger set of possible

confounders. Based on a sample of      n = 27250 two-car accidents where exactly one driver was injured the
unadjusted odds ratio for covariate “Golf-3 yes/no” was estimated at

                                 n10
                         ψ=
                         ˆ           = 1246 / 1576 = 0.791
                                 n01

with 95 percent confidence limits 0.734 and 0.852 (so-called Wald confidence limits). This means that as
compared to drivers of other cars the odds for Golf-3 driver injury is about 21 percent lower (1 – 0.791 =
0.209). In view of this result Golf-3 can be considered as “significantly safer than opponent”.

As the Table 5 shows, the estimated odds ratio for car make and model changes in magnitude if in addition
to car make and model other covariates are included in the fixed effects logit model. The difference between
the unadjusted odds ratio (regression model M1) and the adjusted odds ratio (regression models M2 and
M3), however, is not statistically significant. As can be seen the corresponding confidence intervals overlap.

Table 5          Fixed effects logit model of car diver injury


                                                   Odds ratio for covariate                 Relative length
Model     Covariate(s)
                                            LCL           Estimate              UCL         of CI1)



M1        Golf-3 yes / no                  .734              .791               .852              .149



          Golf-3 yes / no                  .700              .756               .816              .153
M2
          male driver yes / no             .462              .480                                 .075
                                                                                .498

M3        Golf-3 yes / no                  .647              .707               .773              .178
          male driver yes / no                               .539                                 .093
                                           .515                                 .565
                                                            1.289                                 .019
          Mass of opponent                1.277                               1.302
          car (100 kg)

1)
     Relative length of confidence interval = (UCL – LCL) / Estimate


The estimation and testing results for the fixed effects logit model M3 yield an adjusted odds ratio for the risk
factor of 0.707. This can be interpreted as follows: (i) given that the opponent car has the same mass as the
Golf-3 and (ii) given that the two car drivers have the same gender, the chance of being injured is for the
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CEA/EC SARAC II                                                             Crashworthiness Rating Systems



Golf-3 driver 29 percent lower (1 – 0.707 = 0.293) than for the driver of the opponent car. As the upper
confidence interval limit (UCL) is far below unity (0.773) the crash performance of the Golf-3 can be
considered as significantly better. In view of the lower and upper confidence interval limits (LCL and UCL,
respectively) the safety assessment of Golf-3 does not substantially change after adjustment for the factors
”driver gender” and “mass of opponent car”.

There may, however, be determinants of car driver injury where the unadjusted and adjusted odds ratio differ
significantly. Among other things the above table shows that the estimated odds ratio for the covariate driver
gender equals 0.480 if we only adjust for car make and model (regression model M2) but is equal to 0.539 if,
in addition, adjustment is made also for mass of opponent car (regression model M3). This result tells us that
being a male person roughly halves a driver’s chance of severe or fatal injury. Looking at the confidence
intervals we conclude that the adjusted odds ratio is different from the unadjusted. The absolute difference
between the two values is, however, rather small (0.539 – 0.480 = 0.059); as driver gender and opponent car
mass are largely independent determinants of driver injury status, this is, of course, not surprising.

It should be noted that in the regression models M2 and M3 the coefficients and thus the odds ratios for the
various determinants of driver injury status are estimated with quite different levels of accuracy. As the
relative lengths of the confidence intervals (length of interval divided by estimate) show, the estimation of the
coefficient for mass of opponent car is by far the most accurate. According to M3, it is almost certain
(confidence level 95 percent) that colliding with a “heavier” car rather than with a “lighter” car (mass
difference 100 kg) increases the driver’s odds of being injured between 27.7 and 30.2 percent.

The practical applications presented above illustrate the potential of matched study designs when used
together with appropriate methods for the analysis of matched pairs data. Obviously, the statistical sciences
offer a broad range of efficient and methodologically sound concepts and tools suitable for empirical car
safety investigations based on two-car accident data.


3.3       Framework for assessing of the relative Performance of various
          Vehicle Crashworthiness Estimators through Data Simulation (ST
          3.2)
Currently there exist a number of methods available for use in the estimation of vehicle safety ratings. The
aim of this sub-task was to relate each of these safety-rating methods to a comprehensive theoretical
framework that describes the process by which the crash data being analysed is generated. It stems from
the consideration of injury risk functions relating crash impact severity to vehicle occupant injury risk as well
as functions describing the distribution of crashes by impact severity. A consideration of how observed
crash data is generated from the physical processes, via the framework, allowed explicit comparison of what
each rating system actually represents. The project also aimed to consider how crash data sampling may
bias each of the ratings as well as assessing the need for, and adequacy of, adjustment processes to control
for the effects of factors besides vehicle model on occupant injury risk.




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CEA/EC SARAC II                                                                   Crashworthiness Rating Systems


The theoretical framework developed in this sub-task is also used to study the relative performance of each
safety rating method using crash data simulated from the developed framework. This allows quantification of
the ability of each rating method to accurately estimate the known vehicle safety performance from which the
simulated crash data was derived.

The Process of Injury Data Generation

The process of injury data generation is primarily a function of two components. The first relates to the risk
of injury at given levels of impact severity whilst the second relates to the distribution of crashes by impact
severity. These two components are considered below.

There is a clear relationship between injury risk to human vehicle occupants and impact severity in a crash.
In much of the research that has been carried out in the area of vehicle safety, this relationship is formalised
as a probability of injury to a vehicle occupant as a function of the crash impact severity. Differential
performance between vehicles in protecting their occupants in the event of a crash is reflected in different
relationships between the probability of injury and crash impact severity. This concept is illustrated for two
different hypothetical vehicle models in Figure 1.


                                      1
                                     0.9
                                     0.8
               Injury Prob ability




                                     0.7
                                     0.6
                                     0.5
                                     0.4
                                     0.3
                                     0.2                                                     P1(S)
                                     0.1                                                     P2(S)
                                      0
                                           0   20    40         60          80         100           120
                                                          Impact Severity


Figure 1                A hypothetical relationship between probability of injury and crash impact severity

In comparing the relative injury probability between vehicle 1 and 2 in Figure 1, it is evident that the occupant
of vehicle 1 has a proportionately lower risk of injury than the occupant of vehicle 2 at all levels of crash
impact severity. It may not always be the case that one vehicle is safer than another at all crash impact
severities however. If, for example vehicle 2 were better optimised in terms of design to protect its occupants
in high impact severity crashes than in low impact severity crashes relative to vehicle 1, the injury probability
curves shown in Figure 2 may arise.




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CEA/EC SARAC II                                                                                                                            Crashworthiness Rating Systems




                                                                    1
                                      0.9
                                      0.8
               Injury Prob ab ility

                                      0.7
                                      0.6
                                      0.5
                                      0.4
                                      0.3
                                      0.2                                                                                                              P1(S)
                                      0.1                                                                                                              P2(S)
                                                                    0
                                                                        0              20        40               60              80           100               120
                                                                                                      Impact Severity


Figure 2                    Alternative relationships between the probability of injury and crash impact severity for
different vehicles

Here, the occupants of vehicle 2 have a higher probability of injury than those of vehicle 1 at crash impact
severities up to 60, and relatively lower injury probabilities at impact severities above 60.

The main aim in presenting Figures 1 and 2 is to demonstrate that an injury probability curve will be unique
to each vehicle model with no necessarily specific relationship between the curves for different vehicle
models apart from each being monotonic.

The second important consideration in understanding how observed crash injury data arise is the distribution
of crash frequency by injury severity. Figure 3 shows hypothetical probability distributions of crashes by
crash impact severity for two different vehicle models.


                                                                          0 .0 3


                                                                        0 .0 2 5                                                                     f1( S )
                                      Prob ab ility Distrib ution




                                                                                                                                                     f2( S )
                                                                          0 .0 2


                                                                        0 .0 1 5


                                                                          0 .0 1


                                                                        0 .0 0 5


                                                                              0
                                                                                   0        20        40             60               80      1 00             1 20
                                                                                                           Im p a c t S e v e r ity



Figure 3                    Probability distribution function of crashes by injury severity



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In the hypothetical probability distribution functions shown in Figure 3, vehicle 1 has a higher proportion of
crashes at lower impact severities whilst vehicle 2 has a higher proportion of crashes at higher impact
severities. In practice, these types of distributions might arise if, say, vehicle 1 is exposed more and hence
crashes more in low speed environments such as densely populated urban areas whilst vehicle 2 crashes
more on high speed open roads.

Ideal Vehicle Safety Rating Measures

In understanding the mechanism of crash injury data generation, it is possible to propose a vehicle safety
rating system for consumer information that measures relative vehicle safety. Consideration of the
probabilistic way in which crashes are generated suggests a number of possible ways in which relative
vehicle safety might be rated.

The rating measure chosen as the benchmark in this study is represented by the integral of the injury
probability curve weighted by the crash distribution curve to give a weighted average injury probability, pWA.

                                                 ∞
                                        pWA =    ∫ p(s) f (s)ds … (Eqn.1)
                                                s =0


This has the advantage in not requiring an arbitrary choice of integral bound and also being of finite value
because of the properties of f(s).     Because f(s) is possibly different for each vehicle model, to make
comparison between each vehicle model a function of only the injury probability curve of the vehicle p(s), a
standardised form of f(s) to be used in calculating pWA for each vehicle model could be chosen. The most
logical choice for a standardised f(s) would be the average across all vehicles appearing in the available
crash data.

Rating Measures Examined

The first stage of the project examines the vehicle safety ratings systems in existence and common use in
the context of the theoretical framework for crash generation described above. Four rating methods used by
the following organisations are examined.

     •   Monash University Accident Research Centre in Australia

     •   Folksam Insurance in Sweden

     •   The Department for Transport in the U.K.

     •   The University of Oulu in Finland

In addition to the above established ratings systems, the study also considers the new ratings system
proposed in SARAC I, sub-tasks 1.6 and 3.4. Dubbed the ‘Newstead method’, this new ratings system was
formulated to produce independent estimates of vehicle crashworthiness and aggressivity from real crash
data where only crashes involving injury are reported. A further new method proposed by Hautzinger as part
of associated SARAC II work and called the Odds Ratio method is also considered.




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Theoretical Comparisons of the Ratings

In reviewing each of the considered measures of crashworthiness and, where defined, aggressivity a number
of common issues have arisen. They relate to the nature of the data available for analysis, key assumptions
underpinning formulation of the ratings measures and the method of adjusting the ratings to standardise the
effects of non-vehicle related factors on the injury outcome.

All but one of the ratings systems considered has been formulated to overcome the problem of only crashes
where at least someone is injured being reported to police. Reflecting this, the two new measures studied
have also both been designed for application to injury only crash data sources. The MUARC rating system
is the exception to this pattern. The difficulty that arises when non-injury data is unavailable is can be
explained with reference to Table 6 below. Table 6 provides a representation of observed crash data
classified into the paired comparison contingency table.

Table 6        Observed crash counts by focus vehicle and other vehicle driver injury outcome over
all impact severity levels
                                                        Focus Vehicle
                                                 Injured           Not Injured
                           Injured           x1 = ∑ x1 ( S )      x2 = ∑ x2 ( S )            ∑ x ( S ) +x ( S )
                                                                                                  1       2
     All Other                                       S                      S                 S
     Vehicles
                         Not Injured         x3 = ∑ x3 ( S )      x4 = ∑ x4 ( S )            ∑ x ( S ) +x
                                                                                                  3         4   (S )
                                                     S                      S                S

                                            ∑ x ( S ) +x ( S ) ∑ x
                                             S
                                                 1       3
                                                                  S
                                                                      2   ( S ) +x 4 ( S )            N

The key challenge in formulating a ratings measure for application to injury only crash data is that one cell of
the observed paired comparison table where both drivers are uninjured (x4 of Table 6) is not complete.
Therefore, each of the measures defined for injury only crash data cannot use the incomplete cell count in
the risk estimator defined. Each method makes certain compromises and assumptions to overcome this.

These compromises and assumptions fall into two different broad classifications that group the measures.
The first classification of measures is those that make assumptions about the proportionality of either the
absolute risk or odds of injury between the focus vehicle driver and the driver of the average collision partner
vehicle. Methods in this category are the Folksam, Odds Ratio and Oulu relative injury risk measures. In
each of these methods, the proportionality assumption is made in order to reduce the expected value of the
measure to one that is independent of the impact severity distribution for each vehicle. The proportionality
assumption achieves this end in each case but it results in measures of crashworthiness injury risk that are
confounded for both the true underlying crashworthiness and aggressivity measures, p1k(S)and p2k(S)
(where k is defined as the vehicle model index). Consequently they are measuring a combination of both the
crashworthiness and aggressivity performance of the vehicle. A corollary of this problem is that any attempt
to define an aggressivity measure along the same lines as the crashworthiness measure proposed invariably
leads to a measure highly inversely correlated with the crashworthiness measure.




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CEA/EC SARAC II                                                           Crashworthiness Rating Systems


The assumption of proportionality and subsequent confounding of the crashworthiness and aggressivity
measures appears to be a fatal flaw in these ratings measures. Prior research clearly suggests that there
are significant differences in aggressivity between vehicle models. Given this, there appears to be no ready
solution to the inherent problem exhibited by these ratings apart from using them only in applications where
vehicles are rated in groups with homogeneous aggressivity characteristics. This might include estimation of
ratings by year of manufacture but would not include ratings by make and model or broad vehicle market
group.

The second classification of methods is those that rely on real or proxy measures of impact severity to adjust
the ratings measures to make them comparable on a common basis for each vehicle. Methods in this class
include the Newstead, DfT and MUARC methods, with each measure being a function of the underlying true
or apparent impact severity curve for each vehicle model. The concept of an apparent impact severity curve
is defined in sub-task report and is a product of certain impact severities not being represented in the crash
data due to the data reporting criteria or the subset of reported data which the method analyses.

Whether the real or apparent impact severity is represented in the data analysed, this will certainly be
different from vehicle to vehicle so will have to be standardised across vehicles to achieve a rating as close
as possible to the defined benchmark ideal rating system. Without explicit measures of impact severity
appearing in the data, it is necessary to use proxy measures of impact severity and an appropriate statistical
analysis method to standardise the ratings. Appropriate and powerful statistical analysis methods exist for
this task so this is not considered a problem for the methods. Proxy measures of impact severity have also
been identified in the typically available data and are commonly used for this task in rating estimation. A
question that remains, however, is how good these proxy measures are at representing impact severity.
This question remains unanswered in this study and should be the focus of future research perhaps. It might
be best answered through the analysis of in-depth crash investigation that contains data on both the proxy
measures as well as a suitable measure of impact severity.

The MUARC method is perhaps the one most amenable to adjustment using proxy impact severity
measures as it is a function of the full impact severity curve for each vehicle. The difficulty in applying the
MUARC method, however, is that it relies upon having access to both injury and non-injury data sources that
are not available in many countries. In contrast, the Newstead and DfT measures are a function of the
apparent impact severity distribution arising from the data subset from which each measure is calculated.
Whilst it may still be possible for the proxy measures to adequately represent the apparent impact severity
distributions, it is more onerous on them to do so well and might require the use of a wider range or more
detailed proxy measures. For reasons described in the sub-task report, the Newstead crashworthiness
injury risk measure is perhaps the more viable of the two injury crash data based methods. However, the
difference between the two measures in practice needs to be investigated further to establish a clear
preference for one or the other.

A further element of the ratings system reviewed is the statistical methods used for ratings adjustment.
These are discussed extensively in the sub-task report and fall into two broad categories: normalisation


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CEA/EC SARAC II                                                            Crashworthiness Rating Systems



methods and logistic regression techniques. Logistic regression techniques represent the more rigorous
statistical analysis methodology but unfortunately cannot be applied to those methods estimating relative
injury risk or injury odds directly.    Coincidently, these are also the methods making the untenable
assumptions about the proportionality of injury risk or odds of injury between the two colliding vehicles in the
paired analysis framework. It is a further reason to not favour use of these methods.

Simulation Analyses

Simulation analyses were conducted under a range of input data scenarios for five hypothetical vehicle
models. The key inputs into the models were individual injury risk and impact severity distribution for each of
the hypothetical vehicle models considered. Results of the simulation analyses generally confirm a number
of the observations about the various ratings methods made in the theoretical comparisons of the methods.
They also serve to establish the general viability of the study approach by presenting an analysis of real
world impact severity distributions and injury risk curves derived from the German GIDAS in-depth crash
data.

The possibility for high compatibility between each rating system measure and that defined by the
benchmark ideal rating measure is demonstrated in the first simulation when the impact severity distribution
is standardised for each rated vehicle and the assumption of proportionality of injury risk curves is
maintained. Under these conditions, all the measures of injury risk produce estimates of relative vehicle
safety that are entirely consistent with the ideal rating measure.

Based on the theoretical analysis results, when the assumption of proportionality of injury risk curves
between the 5 hypothetical vehicles is violated this is most likely to affect the performance of the Folksam,
Oulu and Odds Ratio methods. Although the simulation results generally confirm the problem under this
scenario for these methods, the degree to which these methods fail is not as great as expected. This is
possibly because the degree of non-proportionality in the simulated data under this scenario is not great.

Varying the impact severity distribution between vehicles when the injury risk curves were proportional in the
simulated data highlighted the strengths of the Folksam, Oulu and odds ratio methods under this condition.
Each controlled well for the differing impact severity distributions and produced ratings estimates consistent
with the benchmark. In contrast the methods relying on adjustment for impact severity differences via the
proxy measures did not perform well. It should be noted however, that in assessing the performance of
these measures under this scenario no attempt to adjust for impact severity differences using the techniques
associated with each method was made as it was beyond the scope and resources of the study. This
scenario does however highlight the need to adjust for impact severity to improve the relationship between
the ratings from these methods and the benchmark.

Simulation analysis of the scenario where impact severity distributions vary across vehicle models and the
injury risk curves for each vehicle model are not proportional, none of the methods considered provide
estimates with a high degree of correlation with the benchmark ratings.




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CEA/EC SARAC II                                                             Crashworthiness Rating Systems


The final simulated scenario considered the inclusion of differential aggressivity parameters for each of the
five vehicles whilst the impact severity distributions were held constant and the injury risk curves were
proportional.    As expected from the theoretical analysis, the odds ratio, Folksam and Oulu measures
performed poorly under this scenario. The MUARC method performed the best whilst the remainder were
somewhere in between.       Because the aggressivity parameter creates differing effective impact severity
distributions between vehicles under the DfT, and Newstead methods, an adjustment for differences in the
effective impact severity distributions is needed. The poor performance of these methods relative to the
MUARC method under the altered aggressivity scenario reflects the lack of adjustment in the results
presented. How the methods would perform with adjustment remains to be established.

Analysis of the real world data from the GIDAS database shows what the ratings methods will have to deal
with when using real world data. The data demonstrates that in practice impact severity distributions do
differ significantly between vehicle classes and probably vehicle models. In addition, injury risk curves are
often not proportional across the range of real impact severity levels observed. Consequently, the results of
the simulation analysis using GIDAS data show that each method has problems in reproducing the
benchmark rating. It should be again noted that these results do not include application of the adjustment
process where required by the methodology. A limited assessment of the effects of the adjustment process
have been considered as an extension to the real world scenario by standardising the impact severity
distribution used to simulate the data.     This limited assessment showed that standardising the impact
severity distribution improved the performance of the methods significantly.

Results of the simulation component of this study are limited in the respect that they have only examined the
relative performance of the base ratings measures from the systems studied. They have not considered the
effectiveness of the adjustment processes included in some of the methods and have only considered a
limited number of scenarios, albeit the most important ones that were likely to give the most interesting
results based on the theoretical assessment. What the study has achieved, however, is to establish the
methodology for the use of data simulation to assess vehicle safety rating system performance. This is a
unique contribution of the study. It is clear that the methods could be further extended to generate simulated
data injury outcome data including the effects of non-vehicle factors and proxies for impact severity. This
data could then be used to assess the performance of the adjustment processes for these factors included in
many of the methods. Unfortunately these extensions were beyond the scope of this particular study but are
recommended for further research.

Key Features of an Ideal Rating System

From the analysis presented in this report, it is possible to identify what the key attributes of an ideal vehicle
safety rating system should be. In summary these are as follows.

     •   The data from which the ratings are estimated should;
             o    include a measure of impact severity
             o    have a range of variables that provide good proxies for impact severity if no impact severity
                  measure is available



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               o   have good data on a range of non-vehicle related factors that affect injury outcome and
                   differ from vehicle to vehicle
               o   contain full reporting on both injury and non-injury crashes
    •      The ratings measure used should
               o   not confound the underlying traits of vehicle crashworthiness and aggressivity with
                   independent estimators of each able to be defined
               o   be estimated using statistical methodology that allows simultaneous adjustment of the rating
                   to standardise for differences in impact severity distribution and non-vehicle related factors
                   between vehicles.

The list of ideal requirements for data is not overly extensive. However, none of the data sets to which the
vehicle safety rating measures are applied meets the full list of requirements. No European of Australasian
data set used for ratings has a reliable measure of impact severity and it is not clear how the proxy
measures available represent the underlying crash impact severity.                 Further research needs to be
undertaken to establish how well the available proxy measures represent crash impact severity. Ideally,
further effort needs to be made to include some measure of impact severity in police reported crash data.

Recommendations for Further Research

Results of this study have led to recommendations for further research in the areas of data systems
investigation and extensions to the investigations undertaken in this study. Key recommendations are as
follows:

    •      It is recommended that research be undertaken to investigate the prospect of enhancing police crash
           data sources to include a measure of impact severity. A number of possible measures are currently
           available but a measure as crude as vehicle damage severity may be adequate.

    •      It is recommended that the analysis framework for data simulation developed in this study be further
           extended to investigate the performance of the various adjustment techniques.

    •      Analysis of in-depth data sources including a measure of crash impact severity is recommended to
           assess the adequacy of various proxy measures in accurately representing impact severity. The
           investigation should focus not only on those proxy variables currently being used but also explore
           the use of other variables to improve the representation. The common availability of any further
           identified variables in police crash data sources should be reviewed.

Conclusion

Theoretical and data simulation undertaken in this report has been able to assess the relative performance
of six different vehicle safety rating methods, a number of them in common use internationally. Performance
has been benchmarked against a defined ideal rating measure derived from a hypothesised physical
framework for the generation of observed driver injury outcome data in motor vehicle crashes. The process
of simulating crash injury outcome data using the physical framework has also been established.




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The study has resulted in the methods being classified into two broad classes. The first is those methods
that rely on an assumption of proportionality between injury risk or the odds of injury between vehicles for all
impact severity levels. These methods have been identified as being fundamentally flawed due to their
measures of vehicle safety confounding the underlying crashworthiness and aggressivity of a vehicle in
unfavourable ways. They also rely on primitive statistical methods and assumptions for standardisation of
the ratings for differences in non-vehicle related factors that affect injury outcomes.

The second class of method are those that rely on adjustment for differences in impact severity distribution
as an intrinsic part of the ratings system. These methods generally have the advantage of using measures
that have a low degree of confounding of the underlying vehicle crashworthiness and aggressivity, allowing
each of these properties of the vehicle to be estimated independently. However, they all rely on data to
which they are applied having an explicit measure of impact severity or proxy measures that adequately
represent impact severity. Very few police reported crash databases in existence internationally have the
former whist the quality and adequacy of the proxy measures is often uncertain. This class of methods also
performs best when applied to data including data on non-injury crashes that is also often not available.
Despite these limitations, it is this class of rating system that is recommended for future application in
estimating vehicle safety performance.




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4         Data Sources for Real World Safety Ratings
4.1       Improvement of Data Collection and Scaling Measures
          (ST 1.3/4.3)
This research project set out to examine a number of aspects related to scaling measures and improvement
of data collection for specifying quality criteria for the safety assessment of cars based on real-world
crashes. A number of important findings eminated from this research.

Event Data Recorders (EDRs)

EDRs were shown to have great potential to provide useful detailed crash information not available by
conventional crash reconstruction techniques. This technology is the most reliable method of measuring
impact severity from the crash pulse and can also provide real time information on driver inputs such as
steering and brake use. In addition, EDRs have been shown to be effective in reducing collisions and
correcting driver behaviour.

There is no regulation currently on what data elements and format that EDRs should record. Manufacturers
of this equipment do appreciate that EDRs can provide information critical for crash analysis thus enable
correlating impact severity with injury potential.

Furthermore, data from these systems can assist with the identification of crash occurrence from the use of
Automatic Collision Notification systems and facilitate appropriate triage procedures and medical response in
the event of a crash.

Cheaper memory and more extensive sensors will allow future EDRs to record substantial important crash
information vital for understanding crashes and their causes more fully. This has the capability of significant
improvement in safety generally in the years ahead. Appropriate industry regulation and legal stability is
critical to ensure these data are available and not misused, although the public perception of the benefits of
EDRs must be addressed before their use will become widely accepted.

Availability of In-Depth Databases

A number of government, industry and private sources currently collect in-depth data around the world and a
selection of these is described in some detail in Chapter 3. Many of these databases are commercial and not
currently public availability. While it may be possible to negotiate for cases with database owners, their use
for retrospective assessment of make and model crashworthiness and aggressivity may present difficulties
for some owners.

A number of issues were identified in analysing the details provided by the database owners. These include
the number and age of cases, the type of inspection undertaken, entrance criteria for inclusion of cases, and
impact severity and injury assessment. Of particular note, though, there was a reasonably high degree of
consistency across these data in terms of range and level of detail collected.




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         Table 7           NHTSA recommended minimum EDR data element set (NHTSA, 2004).

Data Element                                                                   Recording interval/time

Required Elements
 Longitudinal acceleration                                                     -0.1 to 0.5 seconds
 Maximum ∆V                                                                    Computed after event
 Speed, vehicle indicated                                                      -8.0 to 0 seconds
 Engine RPM                                                                    -8.0 to 0 seconds
 Engine throttle, % full                                                       -8.0 to 0 seconds
 Service brake, on/off                                                         -8.0 to 0 seconds
 Ignition cycle, crash                                                         -1.0 seconds
 Ignition cycle, download                                                      At time of download
 Safety belt status, driver                                                    -1.0 seconds
 Frontal airbag warning lamp, on/off                                           -1.0 seconds
 Frontal airbag deployment level, driver                                           Event
 Frontal airbag deployment level, front passenger                                  Event
 Frontal airbag deployment, time to deploy, driver                                 Event
 Frontal airbag deployment, time to deploy, FP                                     Event
 Multi-event, number of events                                                     Event
 Time from event 1 to 2                                                        As needed
 Time from event 1 to 3                                                        As needed
 Complete file recorded, yes/no                                                Following other data
 Elements Required If Equipped
 Lateral acceleration                                                          -0.1 to 0.5 seconds
 Normal acceleration                                                           -0.1 to 0.5 seconds
 Vehicle roll angle                                                            -1.0 to 6.0 seconds
 ABS activity, engaged/non-engaged                                             -8.0 to 0 seconds
 Stability control status, on/off/engaged                                      -8.0 to 0 seconds
 Steering input, angle                                                         -8.0 to 0 seconds
 Safety belt status, front passenger                                           -1.0 seconds
 Frontal airbag suppression switch, front passenger                            -1.0 seconds
 Frontal airbag deployment, time to Nth stage, FP                        Event
 Frontal airbag deployment, Nth stage disposal, driver                   Event
 Frontal airbag deployment, Nth stage disposal, FP                       Event
 Side airbag deployment, time to deploy, driver                          Event
 Side airbag deployment, time to deploy, front passenger                 Event
 Side curtain/tube airbag deployment, time, driver                       Event
 Side curtain/tube airbag deployment, time, FP                           Event
 Seat position, driver                                                   -1.0 seconds
 Seat position. Passenger                                                -1.0 seconds
 Occupant size classification, driver                                    -1.0 seconds
 Occupant size classification, front passenger                           -1.0 seconds
 Occupant position classification, driver                                -1.0 seconds
 Occupant position classification, front passenger                       -1.0 seconds

Popular Cars from European In-Depth Databases

A number of particular makes and models of popular cars were identified from records of EuroNCAP tested
vehicles for which there were reasonable numbers of in-depth cases available in German, UK and European
databases. This exercise was undertaken to feed into later research into the feasibility of combining in-depth
cases for comparing with prospective crash test results.
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Alternative Measures of Safety

The final section of this report addresses an earlier call for alternative measures of injury outcome to those
currently used in existing retrospective rating systems. A number of these measures were discussed in terms
of their strengths and limitations and the implementation of these in suitable databases was also explored.
While it is recognised that many of them have the potential to provide a more detailed assessment of injury
outcome for use in rating safety, they are of somewhat limited use without the inclusion of relevant additional
details in existing mass databases.

            Table 8          Range of alternative measures of severity of outcome available for CW
 •       AIS or MAIS injury score                             •     ICSL (Injury Cost Scale with fatalities
 •       ISS (Injury Severity Score)                          •     Harm Scale
 •       FCI (Functional Capacity Index)                      •     PTS (Poly Trauma Schlüssel)
 •       IIS (Injury Impairment Scale)                        •     GCS (Glasgow Coma Scale)
 •       ICS (Injury Cost Scale / without fatalities)         •     ICD 9 (CM)
It was recommended that in any future research into the Safety Rating of passenger vehicles by SARAC,
that resources be made available to trial the use of alternative measures of safety and in-depth data more
fully.


4.2          Use of VIN for Identifying Vehicle Model and Safety Equipment
             (ST 1.1.)
The evaluation of car safety is difficult because not only are there a large variety of models of car in a
particular marketplace, but also within each model there are a variety of classes and options available.
Without controlling for the particular model type or the safety features it includes leaves open the possibility
of misinterpretation of its safety performance. Mass crash databases commonly used today do not include
this level of detail of their crashed vehicles. However, if these details could enable more sophisticated
retrospective rating analyses to be undertaken. The Vehicle Identification Number (VIN) uniquely identifies
vehicles manufactured worldwide and also includes some details on what various passive safety features are
included in individual vehicles. It therefore may offer the possibility of identifying particular model type and
whether a car possesses certain safety features. Supplementary details with VIN may also help to identify
additional active safety features.

As part of the activities undertaken by the Comité Européen des Assurances, SAfety Rating Advisory
Committee (SARAC), a review was undertaken of VIN structures across various European, US and Asian
countries to examine 1) formal specified systems of VIN worldwide, 2) how it is used by manufacturers
across the various countries, 3) an analysis of safety feature identification patterns in VIN use, and 4)
systems in place to assist researchers identify what safety features are available.

This analysis showed that VIN is not applied in a uniform manner across the different countries and different
manufacturers (see Table 9). While there are consistent features specified by most governments such as
country of manufacture, build date, Vehicle Descriptors, etc., other variables including safety feature details
are ad hoc across the VIN structure. Hence, systematic use of VIN for inclusion in an analysis of safety



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features or for use in controlling for such features in a rating system is impossible without considerable effort
and cooperation from all manufacturers.

Moreover, the analysis also identified a shortage in the collection of VIN information in many of the mass
databases used for such analysis. In other words, even if VIN could be decoded successfully, it will not be
useful for crash databases that do not list the VIN number.

Even if the Vehicle Identification Number (VIN) can be decoded to provide some limited information about
vehicle safety features, more detailed information on optional equipment specification requires access to
databases held by the manufacturers. It is possible, with manufacturer cooperation, to get from VINs the
corresponding Vehicle Order Numbers (VONs) that allow the re-creation of the information on the window
stickers that are on all new passenger vehicles in showrooms (see Figure 4).              Thus, optional safety
equipment can be identified this way.




                     Figure 4 VON Plates from Chevrolet Malibu and Toyota Sienna 101 in USA.

This report highlights the inadequacies of VIN for use in safety analyses in countries apart from the USA
where the collection and interpretation of VIN is mandated by NHTSA. Recommendations are made for
further work to formalise this process in Europe and other countries for use in improved vehicle safety
analyses.




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                                                 Table 9               Sample of VIN number structures for cars manufactured in Europe

                                                                                                            V IN Position Number
                                                             1 to 3      4            5           6           7        8       9   10   11            12   13 to 17                    Le ge nd
    Standards         FMV SS 115 and Part 565                 WMI                   V DS - L, S, B, E, R                      C    Y     P                 #            #     Serial Number
                      SA E J272                               WMI                                 V DS                                         V IS                     A     A irbags
                      ISO 3779                                WMI                                 V DS                                         V IS                    AL     A ssembly Line
    European          BMW                                     WMI                  V DS - S, B, E                      W      C    Y     P                 #            B     Body Type or Style
    Markets           FORD                                    WMI        B            X           X           AL       P      M    B     Y            Mn       #        Bk    Brake System
                      HONDA                                   WMI                  L, E                       T        G      C    Y     P                 #            C     Check Digit
    North A merican   A CURA                                  WMI                      V DS - M, B, T                         C    Y     P                 #           Ca     Cab Type
    Markets           BMW                                     WMI                  V DS - S, B, E                      R      C    Y     P                 #           Ch     Chassis
                      FIA T-FERRA RI                          WMI        E            R                 M              Sp     C    Y     P                 #            D     Destination
                      GENERA L MOTORS                         WMI              L                  B           R        E      C    Y     P                 #           DS     Design Sequence
                      HONDA (passenger cars)                  WMI                      V DS - M, B, T                         C    Y     P                 #            E     Engine Type or Size
                      HONDA (Passport MPV )*                  WMI GV WR, Bk         Ch, Ca        S           B        E      C    Y     P            D        #       EC     Emissions Control
                      HY UNDA I                               WMI        L            B          TL           R        E      C    Y     P                 #            G     Grade
                      INFINITY                                WMI        E            L          MC           B        R      C    Y     P                 #          GV WR Gross V ehicle Weight Ratio
                      ISUZU                                   WMI GV WR, Bk         Ch, R         S           B        E      C    Y     P                 #            L     Line
                      JA GUA R                                WMI      Mk, #         T, St             L, B            EC     C    Y    L, P          M        #        Lx    Luxury Level
                      LEXUS                                   WMI                   V DS - E, G, B, R, L                      C    Y     P                 #            M     Model
                      MA ZDA                                  WMI                     V DS - L, B, E, R                       C    Y     P                 #           MC     Model Change
                      MERCEDES-BENZ                           WMI                         M                            R      C    RY    P                 #           Mk     Market
                      MITSUBISHI                              WMI        R                L, S                B        E      C    Y     P                 #           Mn     Month
                      NISSA N                                 WMI        E            L          MC           B        R      C    Y     P                 #            P     Plant
                      PORSCHE                                 WMI        S            E           R                M          C    Y     P                 #            R     Restraint Type
                      SA A B                                  WMI        L           S, R         B           T        E      C    Y     P                 #           RY     Registration Y ear
                      SUBURU                                  WMI        L            B           E           M        R      C    Y    P, T               #            S     Series
                      SUZUKI (Sw if t and Esteem)             WMI        L         S, Ch, R       E           DS       B      C    Y     P                 #           Sp     Specif ication
                      SUZUKI (V itara and Grand V itara)      WMI                            L, B, E                          C    Y     P                 #            St    Steering
                      TOY OTA                                 WMI                   V DS - L, S, B, E, R                      C    Y     P                 #            T     Transmission / Gearbox
                      V OLKSWA GEN / A UDI                    WMI        S            E           R                M          C    Y     P                 #            TL    Trim Level
                      V OLV O                                 WMI        S           B, R               E              EC     C    Y     P                 U            U     Unique Chassis Number
    Other             HOLDEN                                  WMI              M                 Lx                B           E   Y     P                 #           V DS   V ehicle Descriptor Section
                                                                                                                                                                       V IS   V ehicle Indicator Section
                      * A ll Models have A ctive (Manual) Belts w ith Driver and Passenger Inf latable Restraint                                                        W     Weight
                                                                                                                                                                       WMI    World Manuf acturer Identif ier
                                                                                                                                                                        X     A lw ays X
                                                                                                                                                                        Y     Model Y ear




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42
                                                            Exposure Data “Primary Safety” and Fleet Effects
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5         Exposure Data “Primary Safety” and Fleet Effects on
          Vehicle Safety
5.1       Aspects of Primary Safety (ST 3.1.)
The aim of SARAC 2 sub-task 3.1 is primarily to present a review of primary safety risks and exposures. The
report discusses also about availability and requirements of information needed in primary safety research. A
group of primary safety devices and technologies is also presented. Statistical methods for detecting safety
effects of primary safety devices are discussed as well.

The traffic system should be safe to those who obey rules and regulations. Correct behaviour in the traffic
should not lead to deaths even in case of human error. However, the fluent transportation of passengers and
goods should be guaranteed. This target can be approached by improving the road infrastructure but also by
developing primary and passive safety of cars.

Passive safety of passenger cars has improved with giant steps during the last ten years. The passive safety
level of modern passenger cars can be described as good today. Achieving another equal improvement
during the next ten years is difficult to realize with reasonable efforts.

The overall numbers of accidents have not decreased due to increased traffic volumes. However, the
accident risk in relation to the number of road users and total kilometres driven has decreased. Furthermore,
the improved passive safety of cars is reducing the risk of severe injuries or death continuously when the car
fleets are renewing.

A modern safety structure and versatile equipments of a car have increased the repairing costs. Repairing
costs of apparently light accident without personal injuries may easily be one fourth of the value of a
complete new car. Therefore reducing the number of accidents is one important target also for insurance
companies.

Primary safety technologies seem to have a great potential for the next remarkable safety improvements
such as the present credits of passive safety. Intervention in the key factors and prime causes of the
accidents would reduce the numbers of the accidents overall but also reduce the numbers of injured or killed
persons. At least, an ability to recognize the unavoidable accidents would help to make the consequences of
the accident lighter. Reducing the number of accidents has also positive economical and social influences.

Potential of Primary Safety technologies

Due to high (and mainly unused) safety potential of primary safety systems it is reasonable to focus more
resources of research and product development towards to the primary safety. Safety problems of today
which could be influenced by relevant primary safety technologies are for example:

          •   High collision speed;
          •   Side impacts;
          •   Driver’s drowsiness and alertness;



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          •   Risky drivers i.e. drivers affected by alcohol or drugs, speeding, and
          •   Unintentional travelling out from the driving lane for example due to loss of control.
Because of improved passive safety drivers of modern cars have good possibilities to survive after quite
high-speed head-on collisions. Collision speed of some 80km/h in two new car head-on collision does not
necessary lead to deaths caused by collapse of car body structure. However, high collision speeds are
dangerous even if the cars can withstand the collision forces due to injuring effect of sudden deceleration.
Side impacts lead to severe consequences with remarkably slower collision speeds.

Electronic stability programme (ESP) is developed to help to keep the car in driver’s control even in critical
driving situations. In Germany, for example, it is assumed that approximately 25–30% of all car accidents
involving personal injury are loss of control accidents. Main advantage of the ESP is to help avoiding the loss
of control accidents. Furthermore, a lot of side impacts happen when one looses the control and the car
spins to the opposite lane by its left or right side ahead. ESP should decrease the amount of these side
impacts by maintaining the position of front of a car towards to the driving direction.

Several positive safety effect observations are reported in context of ESP. Estimations for risk reduction of
ESP-pertinent accidents varies from 20% to 40% and up. For example, Swedish researchers reported 22%
overall risk reduction and analyses of NHTSA proved 30–35% reduction in fatal single vehicle accident risk
for ESP-equipped passenger cars.

ESP is today available as standard or extra accessory in many middle-size and large car models. In
Germany more than 50% of new cars are equipped with the ESP. However, in the USA only 10% of new
light vehicles are equipped with the ESP.

It would be beneficial to reduce the actual speeds of the cars just before the collision because the collision
forces depend on speed exponentially. Recognizing of the avoidable accident would also give time for
intelligent passive safety systems to prepare the car for the collision for example by closing the windows and
by tightening the safety belts.

Currently, many new cars are equipped with emergency braking assistant (EBA or BAS), which increases
the pressure difference in the car’s brake system very rapidly when emergency braking is detected. The
deceleration will then reach its maximum value in shorter time. French accident analyse suggested that BAS
system would reduce the number of car occupant fatalities about 6.5–9% and pedestrian fatalities about 10–
12%.

There are not complete commercial solutions of automatic braking systems available but the adaptive cruise
control (ACC) is one step towards to such systems. The operational principle of the ACC is to detect and
adjust the distance to the next vehicle by controlling the throttle and the brakes. At present, ACC is available
as optional in few luxury car models.

There are also potential primary safety systems for decrease the risk of accidents caused by drowsiness or
low alertness of the driver. Drowsiness is a common safety problem among truck drivers but it is also a key
factor of many passenger car accidents. Different driver alertness detectors based on detection of eye



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movements, for example, are currently developed in many countries but commercial versions have not been
launched in large scale.

Lane-keeping systems are meant to forestall the car to travel out from the driving lane unintentionally. The
lane departure warning systems warn the driver form leaving the lane and actual lane-keeping systems may
even steer the car back to the lane. The first versions of lane departure warning systems are today available
as optional in very few car models. The function of those systems is based on visual recognizing of the road
edge markings. It is estimated that approximately 10% of road departure crashes in passenger vehicles and
30% of road departure crashes in heavy trucks could be prevented in the USA by the lateral support
systems.

Driving behaviour of risky drivers endanger always themselves but also the other road users. Therefore also
the law-abiding drivers may suddenly get involved to the accident caused by another driver. Several
countries have established provisional programmes for wide use of alcolocks, which prevent the starting of
the car impossible for persons affected by alcohol. Alcolock can for example be an alternative for driving ban
when the driver has been caught from drunk driving.

28–90% reduction in drunken driving has been observed with the presence of the alcolock. The results of the
studies are quite ambiguous, because the target groups in alcolock-researches are typically the drivers, who
are caught on drunken driving before. Furthermore, before and after studies have showed the high risk of
repetition of drunken driving after the use of alcolock was finished.

Speeding is a safety problem in rural areas for example in terms of more severe loss of control accidents. In
urban areas the collision speed of car has a significant role when considering the risk of death of pedestrians
and cyclists. There are three viewpoints in speed limiters when concerning the implementation and the
intelligence of activated limiter. The lightest limiter types only alert the driver when exceeding the speed limit.
The next limiter type can make the exceeding of the speed limit more difficult for example by adding the
counter force of the throttle pedal. The most radical limiters make it impossible to exceed the limit speed by
interacting with fuel system of the engine.

The most radical speed limiters have been used in heavy trucks for years. There are not actual speed
limiters for passenger cars in general use but an optional speed limiter is currently included in the board
computer of some car models.

A large scale field test concerning the use of intelligent speed limiter “active gas pedal” carried out in
Sweden suggested 7–15% reduction in police reported injury accidents if all vehicles would drive like the test
cars did.

There are also many other systems and devices available or under development, which have a great
potential to reduce the accident risk. Promising techniques have been developed for example in lighting and
night vision systems, which make the driving in poor visibility more safely. Also, development of mobile
technology and data transmission systems is leading to many solutions for sharing the information on road
and traffic conditions to the drivers.




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                                                             Exposure Data “Primary Safety” and Fleet Effects
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Primary safety research

Primary safety research is usually based on the analysis of accident risks from different accident databases.
A purpose is to define the risk levels for certain variables, like different car models and different drivers. The
research tries also to define the influence of more detailed variables, like certain devices, in accident risks.

Risk variables determining expected numbers of accidents can be classified in four classes, which are driver,
vehicle, environment and administration. For example, a new car with sophisticated safety devices may
basically be safe to drive but car may still have high risk for accidents if it is used in risky driving environment
and the driver is not capable to acquit from the driving performance. The risk variables define the risk level of
the car or driver for example, in relation to the extent of activity e.g.:

                                 number of accidents / 100 million kilometres driven

There are four basic types of exposures as well: Different kinds of populations operating in the traffic,
characteristics related to traffic and transportation system, time spent in traffic and induced variables, such
as number of operations and amount of experience. The exposures define the amount of accident potential
the driver is exposed to e.g.:

                     kilometres driven* number of accidents / 100 million kilometres driven

Some of the exposures are measurable or detectable today but a lot of improvement in data collecting
methods is needed. For instance, extensive information on annual kilometres driven by car model, which is
essential for accident risk research, is available only in few countries. Furthermore, there is not sufficient
information available on typical driving environments of car models. Also, it is difficult to find the driver
profiles by car models.

One of the main problems of current risk variable analysis is a lack of harmonized information on car
equipments. In many cases the equipments and systems are standard or optional simultaneously, depending
on car model. So it is very difficult to identify the equipments installed into a single car, because any
revealing coding is not available.

Doubtless, the driver is the most important factor when concerning the errors leading to the accident.
However numerous other variables are influencing to the errors in positive or negative way from the point of
primary safety view. The level of the influence of many variables as well as the interactions between different
variables is still unclear. Therefore, successful primary safety research requires a lot of knowledge from
several fields of transportation. When examining the safety influences of a driver, a car and the road
environment as separated unities on one hand, and as wholeness on the other hand, information for
example on human behaviour and road engineering is needed.

It would be important to identify the risk variables that have the most significant role in accident causation.
Describing the rough basic risk levels to those variables would make it possible to compare and classify the
research targets according to their main primary safety differences. The rough risk levels of the key variables
can later be corrected when information on the safety effects of the other variables is available.



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Conclusions and future proposals

From the primary safety research point of view, it would be important to improve and harmonize
internationally the transportation data recording methods. Primary safety research cannot run effectively
without proper exposure data. The harmonizing would be possible to execute because in many countries
only small amounts of the data is recorded. In most countries such data is not currently recorded at all.
However, usually the problem is not the availability of the data but the coordinated recording practise.
Continuous co-operation between the researchers, car manufacturers and different administrations is
therefore essential.

Information on annual kilometres driven per car model is needed the most desperately for safety ratings
today. This data makes it possible to describe and compare the accident risks in general. The suitable data
would be collected form cars in context of annual technical inspection, in car repair companies or partly in
travel surveys.

In many countries VIN code (Vehicle Identification Number) is used for identifying the cars. This code
includes a lot of information for example about the motor size and the trim level of the car. However, it is still
very difficult for car manufacturer independent researchers to know which devices and systems are included
in each trim level. Therefore, more information on important equipments and systems should be included in
the VIN code or in another suitable harmonized code.

A lot of information on drivers’ behaviour and other accident causation factors would be easy to collect with
car-installed event data recorders or black boxes. These recorders together with sophisticated detector
system would reveal the for example the true collision speeds and the driver manoeuvres before the
accident. This information would be useful for researchers but also for safety device developers.

The event data recorders are also possible to perceive as a preventive safety device if the recorded data
could be used against to the driver when investigating the traffic offences. However, there are many
questions regarding to the principle of individual protection and to other immaterial rights, which have to be
solved before the large-scale use of such data.

Consumers should be advised to emphasize safety issues in their car related selections. This is possible by
making safety issues trendy and by decreasing consumer prices of safety equipments. Moreover, the results
of safety researches should be reliable and easily comparable by consumers. Harmonized primary safety
test protocols are therefore needed. Setting really profitably equipments as standard instead of optional is
also important.

After all, some proportion of the accidents would have been prevented if the drivers would have been aware
of current traffic conditions and exceptions. Several projects targeted to develop in-vehicle information
solutions are currently trying to response to the information lack. Continuous development of mobile
technology is opening new possibilities to inform drivers in their cars.




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5.2         Measuring the Effect of Primary Safety Devices on Accident
            Involvement Risk of Passenger Cars – Methodological
            Considerations
During the Expert Meeting on Primary Safety held in Munich on September 8, 2003 various research designs
for measuring the effect of certain active safety devices (e.g. ESP or ABS) on accident-involvement risk of
passenger cars have been discussed:

1.       Observational Studies

     -   investigations solely based on a sample of accident-involved cars with and without the safety device
         to be evaluated;

     -   investigations based on a sample of both accident-involved and not involved cars with and without
         the safety device to be evaluated.

2.       Experiments

     -   real-world driving experiments with equipped and non-equipped cars under different speed and road
         conditions;

     -   driving simulator experiments of the same type.

Subsequently, some thoughts on the methodology of observational studies are presented. Here, the scope
of possible research designs is limited by the fact that it can be rather laborious to ascertain whether or not a
given car in the sample is or is not equipped with the device under consideration.

Risk Analysis Based on a Sample of Accident-Involved Cars Only

Description of the Approach

In a random sample drawn from the population of all accident-involved cars (more precisely: accident
involvements of cars) vehicles are grouped into cars with and without the device to be assessed. Within each
group of cars vehicles are then further classified according to type of accident. The typology used must be
such that one accident type can be assumed to be closely related to the safety device („device-specific
accident type“) whereas another accident type can be regarded as independent of this device („reference
accident type“).

Under these circumstances sample data may be presented in a 2 x 2 contingency table where rows
correspond to accident types (device-specific, reference) and columns correspond to vehicle equipment
(with, without)

                        with             without

device-specific         a                b

reference               c                d

where a+b+c+d = n. For each row the odds ω can be calculated as follows:



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device-specific odds:      ω(dev) = a/b

reference odds:            ω(ref) = c/d

The effect of the safety device on accident involvement risk is then measured by the odds ratio

(1)                                   ψ = ω(dev) / ω(ref) = (a/b) / (c/d)

The device is assessed to be effective if the odds ratio is significantly less than one.

Discussion

Let M(with) be the population total of vehicle mileage for cars equipped with the device under consideration
and M(without) be the corresponding total for not equipped cars. Further, let R(dev, with) be the risk (per
kilometre) of equipped vehicles to be involved in an accident of the device-specific type and let the quantities
R(dev, without ), R(ref, with) and R(ref, without) be defined analogously.

Then, the population values A, B, C and D of the entries in the above 2 x 2 contingency table may be written
as products of risk and mileage quantities:

                  A = R(dev, with)·M(with)

                  B = R(dev, without)·M(without)

                  C = R(ref, with)·M(with)

                  D = R(ref, without)·M(without)

In case of an „ideal“ reference accident type one would have

R(ref, with) = R(ref, without) ,

i.e. for this accident type involvement risk is independent of vehicle equipment.

The sum A+B+C+D = N corresponds to the total number of accident-involved cars. If a simple random
sample of size n is drawn from the population of accident-involved cars, the expected values of the entries
in the above contingency table are:

                  fA, fB, fC and fD

where f = n/N denotes the sampling fraction.

The population values of the device-specific and the reference odds are Ω(dev) = A/B and Ω(ref) = C/D,
respectively. Considering the counts A, B, C and D as products of risk and mileage quantities as given above,
the population value of the odds ratio may be written as:

(2)    Ψ = (A/B) / (C/D) = (R(dev, with)/R(dev, without)) / (R(ref, with)/R(ref, without))

since the mileage terms cancel out. Consequently, the population value of the odds ratio may be regarded
as a ratio of two relative risks:

Relative risk to be involved in a device-specific accident for vehicles with the safety device (compared to
those without)



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Relative risk to be involved in a reference accident for vehicles with the safety device (compared to those
without)

For large samples the sample odds ratio (1) may be used as an estimate of the population odds ratio (2) as
(1) is an asymptotically unbiased estimator. This means that one can estimate the ratio of two relative
accident-involvement risks (but not the risk or relative risk) when the sample consists of accident-involved
vehicles only.

Additional Remarks

Confounding and interaction problems that may arise can be treated using the standard statistical methods
well documented in the epidemiological literature. Of course, possible confounders and interaction effects
can be investigated in more detail if the sample data stem from in-depth accident studies rather than police
files.

Limitations of the Approach

Using data on accident-involved cars only restricts risk analysis in the way described above. For an objective
assessment of primary safety devices it would, of course, be more informative to obtain for the device-
specific accident type estimates of the absolute risk R(dev, with) or the relative risk R(dev,with)/R(dev,without)
of equipped vehicles. In many cases it could be even more relevant to have estimates of the absolute and
relative accident-involvement risk of equipped vehicles irrespective of accident type, i.e. estimates of the
quantities R(with) and R(with)/R(without). Such estimates, however, cannot be obtained when solely data on
accident-involved vehicles are available.

Risk Analysis Based on Samples of Cars with and without Accident-Involvement (Case-Control
Study)

Description of the Approach

From the population of accident-involved cars a random sample is drawn (selection from files of the national
accident statistics) and for each sampled car it is ascertained whether or not this car is equipped with the
safety device of interest. Accident-involved cars are considered as „cases“.

Similarly, a random sample of cars that have not been involved in an accident during the specified time
period is drawn (selection from the national vehicle register and screening to eliminate accident-involved
cars). These cars are considered as „controls“. As for the cases, for each control it is ascertained whether or
not the corresponding car is equipped with the device to be assessed.

Now, sample data may be presented in a 2 x 2 contingency table of the following form:

                         with            without

accident-involved        a               b

not involved             c               d




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The population values of the cell frequencies may, as before, be denoted by capital letters A, B, C and D.
Since the sampling fractions f and g for cases („with“) and controls („without“), respectively, will normally be
different, the expected values in the sample are given by the following products

                 fA, gB, fC and gD .

In case-control studies where the sampling fractions f and g are not equal (in our context f will normally be
considerably larger than g) only the odds ratio can be estimated, but not risk, relative risk or odds. Therefore,
the odds ratio

Ψ* = (A/C) / (B/D)

is the appropriate measure of comparative chance. Fortunately, accident-involvement is a rare event.
Therefore, A/C is approximately equal to R(with) = A/(A+C) and B/D differs only slightly from R(without) =
B/(B+D). Thus, the odds ratio Ψ* is a good approximation to the relative accident-involvement risk

(4)              Q = R(with) / R(without)

of cars equipped with the device as compared to cars without the safety system of interest.

Consequently, the population odds ratio (3) and the relative risk (4) may be estimated by the sample odds
ratio

(5)              ψ* = (a/c) / (b/d) .

If necessary, the above measure of comparative chance of accident-involvement can be calculated also for
certain types of accidents, especially for the device-specific accident type.

Discussion

Accident-involvement is, of course, not only affected by the dichotomous risk factor „car with/without safety
device of interest“. A main determinant of the cell frequencies in our 2 x 2 table is car mileage. If average
annual mileage differs between cars with and without the primary safety device under consideration the
above comparison is biased.

To account for structural differences of the type described above one can use multiple logistic regression
models to analyse the data. In such models the case-control status of a sample unit (involved / not involved
in accident during study period) is the binary outcome variable whereas vehicle equipment and vehicle
mileage (kilometres driven during study period) are explanatory variables.         Such an approach requires
mileage data on the sample vehicles to be ascertained. In principle, this will be possible by interviewing the
holders and/or drivers of the cars in the study. If a mileage survey of this type cannot be conducted, one
could use vehicle characteristics known to be correlated with mileage and car use (e.g. age, motor power,
make and model etc.) as additional explanatory variables in the logistic regression model.

Conclusion

Appropriate statistical methods exist to assess the effect of primary safety devices on accident-involvement
risk of cars. Ideally, risk analysis should be based on two independent simple random samples of accident-



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involved (cases) and not involved (controls) vehicles that belong to the same general population. Under such
a study design the relative accident-involvement risk of cars equipped with the device of interest can be
estimated (as compared to cars that are not equipped with the system).

If for some reason only a simple random sample of cars that actually had an accident during the study period
can be drawn, meaningful risk analyses can be conducted provided that an appropriate „device-specific“
accident type and a „reference“ accident type can be defined. In this case, an odds ratio can be estimated
that may be interpreted as a ratio of two relative accident-involvement risks (one related to the device-
specific and the other related to the reference accident type). Not surprisingly, however, this indicator
contains less information than the classical relative risk measure to be obtained from the case-control study
outlined above.


5.3       Occupant and Fleet Effects (ST 3.3)
Car safety ratings have been done over 15 years in several countries. During the years driver populations,
car fleets, car use and traffic cultures have changed a lot and in different way in different countries.
Therefore it is important to examine how do the results of car safety ratings in different countries correlate
with the characteristics of changing car fleets and driver populations

Large samples of accidents are always needed for safety ratings and other safety analyses by car models.
This leads to a long observation period that the sample would be large enough for the ratings. However, the
time period can not be too long because several time-dependent factors like driving habits, driver
populations, traffic volumes and regional car use, car mass and design, safety devices and developing traffic
environment, for example, are influencing on the results. The length of inspection period has in most car
safety ratings been from 5 to 8 years.

Because each rating is an individual analysis regardless the chosen method, also the results present only a
certain cross-section based only on the analysed data set. This means that it is not possible directly to
compare the results even between two consecutive ratings from two different data sets in the same country.
Comparisons between different countries are still more unreliable because of typically different
methodologies and data recording systems. Therefore it is more illustrative to show examples from different
kind of progresses in driver and fleet distributions of different countries during the past 15 years. They
describe and at least give hints about different changes and tendencies and possible variables behind them.
These examples will describe therefore more or less the phenomena and their directions affecting on
changing fleets and occupant populations and furthermore on safety ratings. This perspective offers a
possibility to consider occupant and fleet effects on rating results more versatile using also smaller data sets.

It is essential to remember that the presented examples that are based on the accident data are not valid in
the same extent in all countries. The report also concerns briefly the problems in the interpretations of
different ratings.




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Occupant effect

The occupant effect is polarized to the occupants of different age and gender and their different propensities
to involve in certain types of accidents and to injure in those. The owner profiles vary significantly between
the car models. Female favour small cars and in general a proportion of females as car owner grow when
the car gets older. As an example some about 38% of cars are owned by females in Finland. However, there
are large differences between car models. Some 60% of certain car model owners are females and old
females can be the largest owner group. On the other hand there are some new large car models which are
owned practically only by middle-aged men. Some models are very popular among young drivers. This type
of variations can be seen in each European country but the differences vary between car models and user
age groups. Large departures from the average fleet reflect also to the results of ratings, but the absolute
extent of the bias is impossible to calculate from accident data.

A difficult problem in safety ratings is to identify the real car drivers in the traffic, their mileage and their
experience in the traffic. In accident statistics we often have only the information of injured occupants
available, but seldom any complementary pieces of information. In different age groups the proportions of
driving license holders and car owners differ largely but still more is varying their distributions of accident and
injury involvements. Young drivers have much more accidents and injuries than their proportion as driving
license holders and car owner would presume. In the age group of 45 to 64 years the figures are just
opposite.

However, 40% of injured drivers in crashes did not own the vehicles they were driving.

Table 10          Licences: proportion of driving licence holders by gender and age related to all
driving licence holders. Average 2001-2003. Car owners: proportions of passenger cars by owners’
gender and age. End of year 2002. Accidents: proportions of drivers involved in accidents as guilty.
Injured drivers: proportions of injured drivers in all accidents. Accident data from years 2001-2003.

                                   Male                                     Female
                     18-24    25-44    45-64       65-84      18-24     25-44   45-64        65-84
              Age
                      [%]      [%]      [%]         [%]        [%]       [%]      [%]         [%]      All [%]
Licences               6        21       21          7          5         19       17          3        100
Car owners             5        27       28          10         2         12       12          3        100
Accidents*             17       23       17          7          6         12       8           2        100
Injured drivers        17       20       17          9          8         13       10          3        100
*guilty

It can be suggested that some car models have larger proportion of risky drivers than the others. For
example a majority of owners of small car are female who have higher injury and injury severity risk than
male. Furthermore, a high proportion of young drivers increase the accident risk and injury risk of old cars.
The increasing proportion of female owners increases the computational injury risk of old cars.

Over 65 years old occupants are more vulnerable than younger ones. A person 65 or older who is involved
in a car accident is more likely to be seriously injured, more likely to require hospitalisation, and more likely
to die than younger person. If the vulnerable drivers use old and small cars, like in this study shown, it is




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quite obvious that the drivers will get injuries in accidents. The risk impact on the fleet level in traffic,
however, is not linear according to the age of driver population, because the youngest and the oldest drivers
tend to have less mileage and they are using their smaller and older cars for different purposes and in
different times.

The average age of car drivers in EU is increasing. The ageing is especially rapid in the countries with high
car ownership rate and high rate of driving licence holders. This will obviously increase both injury and injury
severity risks in Europe if we cannot compensate the growth with still safer cars, with quicker renewing of
European car fleets, with softer road environments and with advanced accident preventive technologies.

Fleet effect

Fleet effect describes the influence of renewing and safer car fleet changes and how they influence on the
risk rates in traffic accidents. Typical variables affecting on the safety level of the vehicle fleet are the
changing mass, age, mileage, operation regions and driver populations of the fleet and its sub-groups.

During the previous 15 years investigation period all car manufacturers have developed the structures,
control techniques and safety restraints of their products actively. Especially the planning activities of new
structures, which are able to absorb better the crash energy in frontal and side crashes, have been
beneficial. These have increased the mass of the cars in an average with almost 200 kilograms since 1990.
At the same the relative mass difference between “large” and “small” car has diminished. It is interesting that
for example in France and Spain where the average mass of the fleet has all the inspection period been
much lower compared to Sweden, Germany or Finland, the average mass has increased also roughly with
those 200 kilograms.

New car models seem to be even 40-50 per cent safer than their predecessors some 15 years ago
according several analyses done in different countries. These kinds of conclusions are available in several
European countries. In the Swedish car fleet 1994-2002, the decrease of the relative injury risk has been
continuous and together 18 percent during those nine years. The result is very strong when take into the
consideration the fact that the Swedish fleet is perhaps the most homogeneous in Europe.



Especially strong has been the decrease of injury severity rate. In Sweden the relative fatality risk for car
models launched in 1995-1999 is 86 per cent lower and disability risk 29 per cent lower compared to models
launched in 1980-1944. The improvement has been continuous and very quick among the models of the
1990´s. The relative disability rate has reduced in different AIS classes also more than 72 per cent between
the before mentioned groups. Only the minor injuries (AIS 1) increased by 18 percent. An interesting remark
is that 83 per cent of all injuries of the newest models belonged to the lowest severity class AIS 1.




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Table 11                     Relative disability and fatality risks at different AIS levels (Kullgren, 2002)

                                    Relative disability and fatality risk                          Difference 1980 to 1995

           AIS level            1980-84       1985-89          1990-94         1995-99                  (%)

                  AIS1           204,5         244,1            255,5           240,7                         +18

                  AIS2            57,3          40,4            30,4            16,1                          -72

                  AIS3            73,7          60,1            35,7            18,5                          -75

                  AIS4            12,6           6,5             3,5             1,0                          -92

                  AIS5            33,9          22,9            19,5             9,2                          -73

                  AIS6            25,8          21,5             6,8             3,5                          -86

               AIS2+             203,4         151,3              96            48,3                          -76

                 Total           407,9         395,4            351,5          289,0                          -29

There is strong evidence of an improvement in estimated crashworthiness for newer cars. The safety
performance of newer model generations is much better compared to their predecessors and the trend is
continuous. In German accident data when comparing the crashworthiness rates over consecutive model
generations of a certain car model the newest models, launched in 1999-2002, do have typically 20-40 per
cent lower CWR rate compared to 5-10 years older generations, 30-50 per cent lower compared to 10-15
years old ones and 50-70 per cent to 15-20 years old generations, respectively.


                        20
                        18
  Crashworthiness [%]
  Estimated Adjusted




                        16
                        14
                        12
                        10
                         8
                         6
                         4
                             VW Golf-1         VW Golf-2           VW Golf-3           VW Golf-4
                                                       Vehicle Model


Figure 5                     Estimated crashworthiness rates of the VW Golf over four model generations in Germany
with 95 % confidential limits (Delaney et al. 2005).

In spite of the increased mass of newer models a very positive issue is that newer models are also less
aggressive compared to the older ones. This might reflect an improved safety performance of newer models
but also a decrease of relative differences in masses between small and large cars. The reduction of



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aggressivity has, however, been much lower than the improvement of secondary safety of the inspected car
itself.



                      5

                      4
 INJURY RISK
 IN OPPOSITE          3
     CAR
                      2
                      1975       1980      1985      1990       1995      2000      2005
                                           FIRST YEAR OF USE


Figure 6        Car aggressivity related to the first year of registration. Two-car crash data from Finland
1997-2003 [injuries/100 accidents] (Kari et al. 2005).

In Europe relative size differences of the car fleets have become smaller, which has influenced positively on
occupant safety in both vehicles in two-car crashes. This kind of positive trend of car compatibility should be
continued also in the future. Thought the consumers tend to replace their smaller cars with larger and
heavier cars, the narrowed dispersion of mass in car fleet has also helped to reach the safety targets.
However, there are already some signals of less harmonious fleet from USA and Australia where the
proportions of pick-ups, vans and large SUV and MPV are increasing. Therefore we should try in Europe to
avoid the re-polarization of the fleet. In the U.S. and in Australia there already are observations about
increasing proportions of large SUV, MPV and pick-ups but also smaller city cars in the fleet.

A certain time-depended regional movement among car fleet can be seen. New cars are often registered to
large cities and regional centres from where they slowly move to smaller municipalities and rural areas. The
car fleet is older on rural areas. This is problematic for the traffic safety because the major part of mileage of
older vehicles takes place on rural roads. Older fleet, higher speed limits and older driver population is a very
bad and unrewarding combination, which also increases the risk rate of older fleet. At the same time the
most mileage of the newest fleet is accumulated on urban areas and driven by middle-aged male drivers that
improve the risk rates of newer models in relation to the older ones. Unfortunately it is not possible to
calculate the absolute deviations of these phenomena by car models based on actual databases.

The annual mileage varies by model and age of the car. The mileage of the smallest size class is typically
20-25% lower than the average mileage of the whole fleet. The annual mileages of new cars are the highest
and the drop of the mileage for older cars seems almost linear after third year of use. 10 years old vehicles
tend to have mileages of 60-65 per cent and 20 years old only 30 per cent compared to corresponding new
cars. If the risk calculations are made per driven mileage the differences of risk rates between small and
large as well as between new and older cars grow. The cars that are classified to professional or company
use are younger and they do have much higher mileage compared to privately owned ones. There are large
differences in average mileages between different European countries.


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Car mass influences clearly on injury risk, which is clearly detectable in ratings from UK, France and
Germany. The influence of car mass on injury severity risk, however, is not necessarily obvious. The severity
risk rates do not seem to differ by the size of the car model, even though single differences between car
models can be seen. One possible explanation might be the different use of small and large cars. Smaller
cars have accumulated their most mileage in safer urban traffic but heavier cars do have higher proportion of
long distance traffic.

From the traffic safety research point of view, it would be important to harmonize internationally the
transportation data recording methods. One major problem, for example, is the lack of relevant mileage
information by individual cars. The harmonizing would be possible to execute because in many countries
only small amounts of the data is recorded. In most countries such data is not currently recorded at all.
However, usually the problem is not the availability of the data but the co-ordinated recording practise.
Continuous co-operation between researchers, car manufacturers and different administrations is therefore
essential.

Recommendations

Protection of car occupants

Driver populations in most EU-countries are ageing quickly and the proportions of female drivers in traffic are
increasing. Both of them influence on increasing injury and injury severity risk. During the past 15 years
newer car models have become all the time safer than their predecessors because of their more advanced
structures and passive safety devices. It seems, however, there is less probable to improve vehicle safety
any more as much as in the last decade by design and structural means. Therefore we should pay more
attention to accident preventive technologies and try to quicken the regeneration the fleets with vehicles
equipped with new primary safety systems like ESP, ACC, BAS, etc. Better information about the benefits of
those technologies should be given to the consumers; the technologies should be widely available also in the
smaller and cheaper car models and the taxation systems in member states should not be restrictive for
safety systems.

Naturally all efforts to create less complex and also still softer traffic environments must be continued to
decrease the numbers of victims in European traffic.

Harmonization of car fleets

In Europe relative size differences of the car fleets are getting smaller, which has influences positively on
occupant safety in both vehicles in two-car crashes. This positive trend of car compatibility should be
continued. Therefore we should try in Europe to avoid the re-polarization of the fleet. In the U.S. and in
Australia there already are observations about increasing proportions of large SUV, MPV and pick-ups but
also smaller city cars in the fleet.

Developing of vehicle and accident data

Urbanisation, centralisation of commercial activities and leisure time are increasing in the EU. All these are
influencing on car use, car choice and travel behaviour. In different countries the changes in traffic



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circumstances vary but also the databases describing the characteristics of drivers, car owners, vehicles,
accidents, trip purposes, mileages or road and street classifications have been recorded based on different
criteria. In spite of good intentions congruent statistics even to identify car models and their dimensions
internationally do not exist. For risk analyses one major problem is the lack of relevant mileage information
by individual cars. This information can be recorded in connection to yearly car inspections as done in some
member states. The information of when or where or in what kind of use the mileages are accumulated is
important, but there are, for example, no comparable travel surveys available for these purposes. Therefore
the methods of recording vehicle and accident data should be harmonised in the EU.


5.4      Improved Safety by Car Model Series (ST 3.3.- 2)
This report describes analysis undertaken as part of SARAC II sub-task 3.3 aimed at assessing changes in
the safety performance of individual vehicle types of the same make and model over a number of model
generations. It might be expected that the introduction of a new vehicle model generation would result in an
improvement in the safety performance of that vehicle model. That is, a new generation vehicle model would
be expected to have better safety performance than the previous generation of the same vehicle model.
This study aims to assess this premise by estimating measures of vehicle crashworthiness for a number of
vehicle makes and models over time using real world crash data.

Data sources

Police reported crash data from Germany for the years 1998 to 2002 was used for this analysis.              In
Germany, every road accident attended by the police must be reported and is recorded in a database held at
the German Federal Statistical Office. All crashes reported to police, including those involving only material
damage or slight personal injuries, are included in the database. The sub-set of the data used here is
identical to that used in SARAC II sub-tasks 2.1 and 2.2 and a full description of the data set used is
available in Newstead et al., 2005 (SR-248). Given the volume of data available it has been possible to
examine changes in the safety performance of a range of vehicle makes and models.

Identification of Vehicle Models

The German data used in this analysis did not contain information on the VIN of each crash involved vehicle.
Therefore, selection of vehicle models from the crash data for inclusion in the analysis was conducted using
the manufacturer name and vehicle model name variables (“mancnam” and “modcnam” respectively)
provided in the data.    The vehicle model name variable included a numerical indicator of the model
generation of the subject vehicle. This numerical indicator was used to distinguish between vehicle models
of the same type but of different generations with more recent models having higher generation numbers.

Method

Estimates of vehicle crashworthiness were calculated using the logistic regression procedure described in
detail in SR-248. Estimation of the injury risk component of the crashworthiness rating was conducted using
the DfT method using crash data for crashes involving two passenger vehicles only.              The severity



58
                                                         Exposure Data “Primary Safety” and Fleet Effects
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                                                                                       on Vehicle Safety


component of the crashworthiness rating was estimated using the MUARC method and used crash data
from both single vehicle and vehicle-to-vehicle crashes. In order to obtain estimates of injury risk and injury
severity unbiased by factors other than vehicle make and model a number of factors thought to influence the
risk and severity of injury to drivers were included in the logistic regression model. Those factors considered
were the same as those used in sub-tasks 2.1 and 2.2 for the German data. Of particular importance was the
inclusion of year of crash as an adjustment factor in the logistic regression models. By adjusting for year of
crash effects, the resulting estimates of vehicle crashworthiness within a particular model series will be
unbiased for general improvements in German road safety over the years of crash data available. Table 12
details the main effects and interactions that were judged to be significant predictors of injury risk and injury
severity through the stepwise logistic modelling approach.

Table 12        Significant factors in the logistic regression models of injury risk and injury severity
derived from German data using the DETR method.
   Significant Model Factors           All Crash Types (Injury risk)      All Crash Types (Injury Severity)
                                    driver age (age)                      driver age (age)
                                    driver sex (sex)                      driver sex (sex)
                                    intersection (int)                    number of vehicles (nbv)
 Main Effects
                                    location of crash (loc)               location of crash (loc)
                                    cost of crash (cost)                  cost of crash (cost)
                                    year of crash (year)                  year of crash (year)
                                                                          age*sex, age*nbv, sex*nbv,
                                    sex*age, age*int, age*loc, int*loc,   age*int, sex*int, veh*int, veh*loc,
 First Order Interactions           age*cost, sex*cost, int*cost,         int*loc, age*cost, sex*cost,
                                    loc*cost                              nbv*cost, int*cost, loc*cost,
                                                                          int*year, loc*year, cost*year
                                                                          age*sex*nbv, nbv*int*loc,
                                                                          age*int*cost, nbv*int*cost,
 Second Order Interactions          int*loc*cost
                                                                          nbv*loc*cost, int*loc*cost,
                                                                          int*cost*year
Models were selected for inclusion in the analysis on the basis of a minimum of 100 crash involved drivers
and 20 injured drivers in each vehicle make and model generation. Further, those models included were
restricted to those where more than one generation of the vehicle make and model met the above criteria
and was thus available in sufficient quantities to allow comparison over time. Table 13 shows that there
were 78 vehicles with sufficient real crash data to be included in the analysis including 27 individual vehicle
makes and models.




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                                                                                 on Vehicle Safety


Table 13        Number of injured and involved drivers of crashed vehicles in the German data from
1998 to 2002.
                                                                        Involved
     Vehicle Identification Index           Vehicle Make/Model                     Injured Drivers
                                                                         Drivers
                 A1                 AUDI AG A4-1                          4217          2266
                 A2                 AUDI AG A4-2                           315          152
                 B1                 BMW 3er-1                              102            71
                 B2                 BMW 3er-2                             4713          2715
                 B3                 BMW 3er-3                             7416          4348
                 B4                 BMW 3er-4                             1987          1012
                 C2                 BMW 5er-2                              402          231
                 C3                 BMW 5er-3                             3524          1773
                 C4                 BMW 5er-4                             2239          951
                 D1                 CHRYSLER (USA) Voyager-1               110           49
                 D2                 CHRYSLER (USA) Voyager-2               297          121
                 E2                 FIAT (I) Punto-2                       480           339
                 E1                 FIAT (I) Punto                        3175          2420
                 F3                 FORD/EUROPA Escort-3                  2857          1823
                 F4                 FORD/EUROPA Escort-4                  8390          5172
                 G1                 FORD/EUROPA Fiesta-1                   161          114
                 G2                 FORD/EUROPA Fiesta-2                  1720          1232
                 G3                 FORD/EUROPA Fiesta-3                  9213          6923
                 G4                 FORD/EUROPA Fiesta-4                  3003          2069
                 H2                 FORD/EUROPA Mondeo-2                   185            83
                 H1                 FORD/EUROPA Mondeo                    3787          2042
                 I1                 FUJI HEAVY (J) Legacy-1                211           109
                 I2                 FUJI HEAVY (J) Legacy-2                145            77
                 J2                 HONDA MOTOR (J) Accord-2               227          131
                 J3                 HONDA MOTOR (J) Accord-3               383          189
                 J4                 HONDA MOTOR (J) Accord-4               444          229
                 J5                 HONDA MOTOR (J) Accord-5               101           51
                 K2                 HONDA MOTOR (J) Civic-2                270           178
                 K3                 HONDA MOTOR (J) Civic-3               1305           864
                 K4                 HONDA MOTOR (J) Civic-4               1544          1042
                 K5                 HONDA MOTOR (J) Civic-5                708           494
                 K6                 HONDA MOTOR (J) Civic-6                488           298
                 J1                 MERCEDES BENZ AG C-1                  5024          2594
                 J2                 MERCEDES BENZ AG C-2                   572          270
                 K3                 MERCEDES BENZ AG E-3-T                 210            83
                 L3                 MERCEDES BENZ AG E-3                  1035          467
                 K4                 MERCEDES BENZ AG E-4-T                1310          661
                 L4                 MERCEDES BENZ AG E-4                  6086          3260
                 K5                 MERCEDES BENZ AG E-5-T                 639          255
                 L5                 MERCEDES BENZ AG E-5                  3344          1648
                 M3                 MITSUBISHI (J) Colt-3                  455           311
                 M4                 MITSUBISHI (J) Colt-4                  916           601
                 M5                 MITSUBISHI (J) Colt-5                  702           470
                 M6                 MITSUBISHI (J) Colt-6                  611           409
                 N1                 MITSUBISHI (J) Space Runner-1          140            83


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                                                                                    on Vehicle Safety


                   N2                MITSUBISHI (J) Space Runner-2              303           168
                   O2                NISSAN (J) Almera-2                        109            66
                   O1                NISSAN (J) Almera                          780           470
                   P1                NISSAN (J) Micra-1                        1519          1170
                   P2                NISSAN (J) Micra-2                        1765          1395
                   Q1                NISSAN (J) Primera-1                      1334           744
                   Q2                NISSAN (J) Primera-2                       593           340
                   R1                OPEL Astra-1                             12835          7873
                   R2                OPEL Astra-2                             3192           1865
                   S1                OPEL Corsa-1                             4983           3805
                   S2                OPEL Corsa-2                             9178           6828
                   S3                OPEL Corsa-3                              364            248
                    T1               OPEL Omega-1                             2853           1543
                    T2               OPEL Omega-2                             2159           1053
                   U1                OPEL Vectra-1                            7275           4183
                   U2                OPEL Vectra-2                            3538           1920
                   V1                RENAULT (F) Clio-1                       3763           2661
                   V2                RENAULT (F) Clio-2                        655            473
                   W1                SEAT (E) Ibiza-1                          1249           832
                   W2                SEAT (E) Ibiza-2                          2082          1434
                   X2                TOYOTA (J) Corolla-2                       226           150
                   X3                TOYOTA (J) Corolla-3                     1514            936
                   X4                TOYOTA (J) Corolla-4                     1375            892
                   X5                TOYOTA (J) Corolla-5                       631           400
                   Y1                VOLKSWAGEN-VW Golf-1                      1844          1206
                   Y2                VOLKSWAGEN-VW Golf-2                     19624          13200
                   Y3                VOLKSWAGEN-VW Golf-3                     20401          12514
                   Y4                VOLKSWAGEN-VW Golf-4                      5829          3543
                    Z2               VOLKSWAGEN-VW Passat-2                    2203          1226
                    Z3               VOLKSWAGEN-VW Passat-3                    9045          5115
                    Z4               VOLKSWAGEN-VW Passat-4                    3846          1921
                   AA2               VOLKSWAGEN-VW Polo-2                      8882          6665
                   AA3               VOLKSWAGEN-VW Polo-3                      5986          4289
Results

Injury risk, injury severity and crashworthiness ratings for each of the vehicle models considered are
presented in Table 14 below.    Upper and lower confidence limits and confidence limit width for each
estimated crashworthiness rating are also provided. The coefficient of variation shown is the ratio of the
width of the confidence limit to the magnitude of the point estimate and is useful as a scaled measure of
rating accuracy.




                                                                                                       61
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                                                                                 on Vehicle Safety


Table 14        Vehicle safety ratings estimated from German crash data 1998-2002.
                                                                      CWR 95% CL
Vehicle Index          Vehicle            CWR      Risk     Severity Lower Upper       95% CL CoV
                       Make/Model                                                      Width
A1                     AUDI AG A4-1       7.00% 53.39% 13.12%        6.34% 7.74% 1.40%          0.20
A2                     AUDI AG A4-2       4.66% 46.83% 9.94%         2.92% 7.42% 4.50%          0.97
AA2                    VOLKSWAGEN-VW      19.45% 78.65% 24.72%       18.45% 20.50% 2.05%        0.11
                       Polo-2
AA3                    VOLKSWAGEN-VW      9.77%    67.36% 14.50%     9.07%    10.52% 1.44%      0.15
                       Polo-3
B1                     BMW 3er-1          14.25%   76.60%   18.60%   8.99%    22.59%   13.60%   0.95
B2                     BMW 3er-2          13.42%   64.47%   20.82%   12.49%   14.43%   1.94%    0.14
B3                     BMW 3er-3          7.84%    57.64%   13.60%   7.33%    8.38%    1.05%    0.13
B4                     BMW 3er-4          5.05%    48.21%   10.46%   4.33%    5.88%    1.56%    0.31
C2                     BMW 5er-2          13.00%   66.52%   19.54%   9.90%    17.07%   7.16%    0.55
C3                     BMW 5er-3          7.22%    53.10%   13.60%   6.50%    8.02%    1.52%    0.21
C4                     BMW 5er-4          3.98%    40.70%   9.77%    3.38%    4.68%    1.29%    0.33
D1                     CHRYSLER (USA)     6.17%    44.39%   13.90%   3.05%    12.51%   9.46%    1.53
                       Voyager-1
D2                     CHRYSLER (USA)     4.30%    36.45% 11.79%     2.73%    6.77%    4.05%    0.94
                       Voyager-2
E1                     FIAT (I) Punto     10.91% 73.33% 14.88%       9.88% 12.05% 2.17%         0.20
E2                     FIAT (I) Punto-2   7.60% 65.84% 11.55%        5.78% 9.99% 4.21%          0.55
F3                     FORD/EUROPA        17.24% 71.33% 24.17%       15.85% 18.75% 2.90%        0.17
                       Escort-3
F4                     FORD/EUROPA        12.22% 64.06% 19.08%       11.50% 12.99% 1.50%        0.12
                       Escort-4
G1                     FORD/EUROPA        20.25% 76.16% 26.59%       14.30% 28.68% 14.37% 0.71
                       Fiesta-1
G2                     FORD/EUROPA        21.46% 77.87% 27.56%       19.26% 23.91% 4.65%        0.22
                       Fiesta-2
G3                     FORD/EUROPA        15.88% 75.84% 20.94%       15.02% 16.79% 1.77%        0.11
                       Fiesta-3
G4                     FORD/EUROPA        10.90% 65.85% 16.55%       9.88%    12.02% 2.14%      0.20
                       Fiesta-4
H1                     FORD/EUROPA        7.75%    55.11% 14.07%     6.98%    8.61%    1.63%    0.21
                       Mondeo
H2                     FORD/EUROPA        4.36%    43.42% 10.04%     2.50%    7.60%    5.10%    1.17
                       Mondeo-2
I1                     FUJI HEAVY (J)     11.11% 54.08% 20.53%       7.95%    15.52% 7.57%      0.68
                       Legacy-1
I2                     FUJI HEAVY (J)     6.94%    48.85% 14.21%     4.35%    11.08% 6.73%      0.97
                       Legacy-2
J1                     MERCEDES BENZ      5.42%    50.58% 10.72%     4.91%    6.00%    1.09%    0.20
                       AG C-1
J2                     HONDA MOTOR (J)    6.91%    50.96% 13.56%     5.39%    8.85%    3.46%    0.50
                       Accord-2
J3                     HONDA MOTOR (J)    8.81%    54.20% 16.26%     6.51%    11.93% 5.42%      0.61
                       Accord-3




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                                                                              on Vehicle Safety


J4                HONDA MOTOR (J)      11.15% 52.93% 21.06%       8.75%    14.20% 5.45%     0.49
                  Accord-4
J5                HONDA MOTOR (J)      5.81%    50.57% 11.49%     3.07%    10.99% 7.91%     1.36
                  Accord-5
K2                HONDA MOTOR (J)      15.95% 72.74% 21.93%       11.70% 21.76% 10.06% 0.63
                  Civic-2
K3                HONDA MOTOR (J)      15.92% 67.61% 23.54%       14.24% 17.79% 3.54%       0.22
                  Civic-3
K4                HONDA MOTOR (J)      11.75% 60.60% 19.40%       10.69% 12.92% 2.22%       0.19
                  Civic-4
K5                HONDA MOTOR (J)      8.58%    53.10% 16.17%     7.38%    9.99%    2.61%   0.30
                  Civic-5
K6                HONDA MOTOR (J)      8.84%    60.14% 14.69%     6.91%    11.30% 4.38%     0.50
                  Civic-6
L3                MERCEDES BENZ        10.66% 55.89% 19.08%       8.73%    13.02% 4.29%     0.40
                  AG E-3
L4                MERCEDES BENZ        8.00%    58.28% 13.72%     7.31%    8.75%    1.44%   0.18
                  AG E-4
L5                MERCEDES BENZ        5.09%    49.86% 10.20%     4.46%    5.81%    1.35%   0.27
                  AG E-5
M3                MITSUBISHI (J)       20.03% 74.62% 26.85%       16.47% 24.37% 7.90%       0.39
                  Colt-3
M4                MITSUBISHI (J)       14.53% 69.47% 20.91%       12.45% 16.95% 4.50%       0.31
                  Colt-4
M5                MITSUBISHI (J)       12.82% 66.25% 19.35%       10.74% 15.30% 4.56%       0.36
                  Colt-5
M6                MITSUBISHI (J)       9.95%    63.25% 15.73%     7.99%    12.38% 4.40%     0.44
                  Colt-6
N1                MITSUBISHI (J)       4.48%    65.14% 6.88%      1.90%    10.58% 8.69%     1.94
                  Space Runner-1
N2                MITSUBISHI (J)       8.16%    54.49% 14.98%     5.80%    11.49% 5.69%     0.70
                  Space Runner-2
O1                NISSAN (J) Almera    9.18% 58.71% 15.64%        7.57%    11.13% 3.56% 0.39
O2                NISSAN (J) Almera-   10.31% 54.94% 18.76%       6.42%    16.54% 10.11% 0.98
                  2
P1                NISSAN (J) Micra-1   21.16% 79.10% 26.76%       18.91% 23.69% 4.77%       0.23

P2                NISSAN (J) Micra-2 15.38% 75.82% 20.28%         13.74% 17.22% 3.48%       0.23

Q1                NISSAN (J)           10.67% 58.87% 18.12%       9.21%    12.35% 3.14%     0.29
                  Primera-1
Q2                NISSAN (J)           6.78%    56.62% 11.98%     5.30%    8.68%    3.37%   0.50
                  Primera-2
R1                OPEL Astra-1         10.43%   62.81%   16.61%   9.85%    11.05%   1.20%   0.12
R2                OPEL Astra-2         6.69%    56.62%   11.82%   5.98%    7.50%    1.52%   0.23
S1                OPEL Corsa-1         18.62%   79.62%   23.39%   17.42%   19.90%   2.48%   0.13
S2                OPEL Corsa-2         11.62%   71.21%   16.32%   10.95%   12.34%   1.40%   0.12
S3                OPEL Corsa-3         7.73%    61.89%   12.49%   5.78%    10.35%   4.57%   0.59
T1                OPEL Omega-1         10.73%   59.11%   18.15%   9.67%    11.90%   2.23%   0.21
T2                OPEL Omega-2         5.11%    49.23%   10.39%   4.37%    5.99%    1.62%   0.32
U1                OPEL Vectra-1        12.00%   63.58%   18.87%   11.22%   12.83%   1.61%   0.13



                                                                                               63
                                                         Exposure Data “Primary Safety” and Fleet Effects
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                                                                                       on Vehicle Safety


U2                      OPEL Vectra-2      7.20% 54.67% 13.17%             6.48% 8.00% 1.52%           0.21
V1                      RENAULT (F) Clio-1 14.94% 69.56% 21.47%            13.87% 16.09% 2.22%         0.15

V2                      RENAULT (F) Clio-2 10.99% 67.80% 16.21%            9.14%    13.21% 4.07%       0.37

W1                      SEAT (E) Ibiza-1      15.87% 70.90% 22.39%         13.95% 18.06% 4.12% 0.26
W2                      SEAT (E) Ibiza-2      10.69% 66.11% 16.18%         9.57% 11.94% 2.37% 0.22
X2                      TOYOTA (J)            19.99% 73.37% 27.24%         15.11% 26.44% 11.33% 0.57
                        Corolla-2
X3                      TOYOTA (J)            15.55% 66.67% 23.32%         13.82% 17.50% 3.68%         0.24
                        Corolla-3
X4                      TOYOTA (J)            10.63% 64.75% 16.42%         9.27%    12.19% 2.92%       0.27
                        Corolla-4
X5                      TOYOTA (J)            8.17%      61.53% 13.28%     6.51%    10.25% 3.74%       0.46
                        Corolla-5
Y1                      VOLKSWAGEN-VW         16.15% 71.15% 22.69%         14.42% 18.08% 3.65%         0.23
                        Golf-1
Y2                      VOLKSWAGEN-VW         16.83% 72.96% 23.06%         16.12% 17.56% 1.45%         0.09
                        Golf-2
Y3                      VOLKSWAGEN-VW         9.68%      61.13% 15.83%     9.22%    10.15% 0.93%       0.10
                        Golf-3
Y4                      VOLKSWAGEN-VW         6.44%      59.48% 10.82%     5.86%    7.07%     1.21%    0.19
                        Golf-4
Z2                      VOLKSWAGEN-VW         14.22% 66.07% 21.53%         12.62% 16.03% 3.40%         0.24
                        Passat-2
Z3                      VOLKSWAGEN-VW         9.86%      58.63% 16.82%     9.23%    10.55% 1.32%       0.13
                        Passat-3
Z4                      VOLKSWAGEN-VW         5.18%      51.43% 10.08%     4.55%    5.91%     1.36%    0.26
                        Passat-4



Those vehicle models with crashworthiness estimates available for three or more model generations have
been selected from Table 14. Individual charts have been created for these vehicle models that show
estimated crashworthiness and associated confidence limits for each model generation.            Overlapping
confidence limits between two or more model generations indicates that no statistically significant difference
can be detected between the crashworthiness of those model generations. However, where the confidence
limits associated with individual crashworthiness estimates do not overlap, as is the case for the majority of
the vehicle makes and models, it can be concluded with 95% confidence that the true crashworthiness of the
vehicle model differs across the relevant generations.




64
                                                                                                         Exposure Data “Primary Safety” and Fleet Effects
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                                                                                                                                       on Vehicle Safety


                                      25.00%




                                      20.00%
 Estimated Adjusted Crashworthiness




                                      15.00%




                                      10.00%




                                      5.00%




                                      0.00%
                                                 BMW         3er-1         BMW         3er-2                   BMW    3er-3          BMW        3er-4

                                                                                               Vehicle Model



Figure 7                                            Estimated crashworthiness of the BMW 3-series over 4 model generations

                                      35.00%




                                      30.00%
 Estimated Adjusted Crashworthiness




                                      25.00%




                                      20.00%




                                      15.00%




                                      10.00%




                                      5.00%




                                      0.00%
                                               FORD/EUROPA    Fiesta-1   FORD/EUROPA    Fiesta-2        FORD/EUROPA    Fiesta-3   FORD/EUROPA    Fiesta-4

                                                                                               Vehicle Model



Figure 8                                            Estimated crashworthiness of the Ford Fiesta over 4 model generations




                                                                                                                                                            65
                                                                                                           Exposure Data “Primary Safety” and Fleet Effects
CEA/EC SARAC II
                                                                                                                                         on Vehicle Safety



                                      16.00%



                                      14.00%



                                      12.00%
 Estimated Adjusted Crashworthiness




                                      10.00%



                                      8.00%



                                      6.00%



                                      4.00%



                                      2.00%



                                      0.00%
                                               HONDA MOTOR (J)   Accord-2   HONDA MOTOR (J)   Accord-3   HONDA MOTOR (J)   Accord-4   HONDA MOTOR (J)   Accord-5

                                                                                                 Vehicle Model



Figure 9                                             Estimated crashworthiness of the Honda Accord over 4 model generations




                                      25.00%




                                      20.00%
 Estimated Adjusted Crashworthiness




                                      15.00%




                                      10.00%




                                      5.00%




                                      0.00%
                                               HONDA MOTOR (J)         HONDA MOTOR (J)        HONDA MOTOR (J)       HONDA MOTOR (J)         HONDA MOTOR (J)
                                                    Civic-2                 Civic-3                Civic-4               Civic-5                 Civic-6

                                                                                                  Vehicle Model



Figure 10                                            Estimated crashworthiness of the Honda Civic over 5 model generations




66
                                                                                                                Exposure Data “Primary Safety” and Fleet Effects
CEA/EC SARAC II
                                                                                                                                              on Vehicle Safety


                                      14.00%




                                      12.00%
 Estimated Adjusted Crashworthiness




                                      10.00%




                                      8.00%




                                      6.00%




                                      4.00%




                                      2.00%




                                      0.00%
                                                  MERCEDES BENZ AG       E-3                    MERCEDES BENZ AG      E-4                  MERCEDES BENZ AG      E-5

                                                                                                      Vehicle Model



Figure 11                                          Estimated crashworthiness of the Mercedes E Class over 3 model generations




                                      30.00%




                                      25.00%
 Estimated Adjusted Crashworthiness




                                      20.00%




                                      15.00%




                                      10.00%




                                      5.00%




                                      0.00%
                                               MITSUBISHI (J)   Colt-3         MITSUBISHI (J)     Colt-4         MITSUBISHI (J)   Colt-5        MITSUBISHI (J)    Colt-6

                                                                                                      Vehicle Model



Figure 12                                          Estimated crashworthiness of the Mitsubishi Colt over 4 model generations




                                                                                                                                                                           67
                                                                                                        Exposure Data “Primary Safety” and Fleet Effects
CEA/EC SARAC II
                                                                                                                                      on Vehicle Safety



                                      25.00%




                                      20.00%
 Estimated Adjusted Crashworthiness




                                      15.00%




                                      10.00%




                                      5.00%




                                      0.00%
                                                     OPEL        Corsa-1                 OPEL          Corsa-2                   OPEL       Corsa-3

                                                                                              Vehicle Model



Figure 13                                           Estimated crashworthiness of the Opel Corsa over 3 model generations




                                      30.00%




                                      25.00%
 Estimated Adjusted Crashworthiness




                                      20.00%




                                      15.00%




                                      10.00%




                                      5.00%




                                      0.00%
                                               TOYOTA (J)   Corolla-2      TOYOTA (J)   Corolla-3       TOYOTA (J)   Corolla-4     TOYOTA (J)     Corolla-5

                                                                                               Vehicle Model



Figure 14                                           Estimated crashworthiness of the Toyota Corolla over four model generations




68
                                                                                                       Exposure Data “Primary Safety” and Fleet Effects
CEA/EC SARAC II
                                                                                                                                     on Vehicle Safety


                                      20.00%


                                      18.00%


                                      16.00%
 Estimated Adjusted Crashworthiness




                                      14.00%


                                      12.00%


                                      10.00%


                                       8.00%


                                       6.00%


                                       4.00%


                                       2.00%


                                       0.00%
                                               VOLKSWAGEN-VW     Golf-1       VOLKSWAGEN-VW   Golf-2     VOLKSWAGEN-VW   Golf-3   VOLKSWAGEN-VW     Golf-4

                                                                                                Vehicle Model



Figure 15                                          Estimated crashworthiness of the VW Golf over 4 model generations




                                      18.00%



                                      16.00%



                                      14.00%
 Estimated Adjusted Crashworthiness




                                      12.00%



                                      10.00%



                                      8.00%



                                      6.00%



                                      4.00%



                                      2.00%



                                      0.00%
                                                 VOLKSWAGEN-VW     Passat-2           VOLKSWAGEN-VW      Passat-3        VOLKSWAGEN-VW   Passat-4

                                                                                              Vehicle Model



Figure 16                                          Estimated crashworthiness of the VW Passat over 3 model generations




                                                                                                                                                             69
                                                        Exposure Data “Primary Safety” and Fleet Effects
CEA/EC SARAC II
                                                                                      on Vehicle Safety


It is clear that, for the majority of vehicle makes and models considered, there is strong evidence of an
improvement in estimated crashworthiness over successive model generations. Further, in a large number
of cases these results are statistically significant. These results support the original hypothesis that a new
generation vehicle model has better safety performance than the previous generation of the same vehicle
model.




70
                                                       Relationship between EuroNCAP Results and Real
CEA/EC SARAC II
                                                                                      World Crashes



6        Relationship between EuroNCAP Results and Real
         World Crashes
6.1      Relationship between Driver Injury Outcomes and NCAP Results
         in Europe and Australasia (ST 2.1/2.2-1)

The broad aim of sub-task 2.1 of the SARAC II project was to update a pilot study of the relationship
between EuroNCAP test results and injury outcome in police reported crashes in Great Britain and France
carried out in SARAC I (Newstead et al, 2001). The sub-task uses updated police reported crash data from
Great Britain and France and newly obtained police reported crash data from Germany to estimate injury risk
and injury severity measures for European vehicles.        The relationship between these measures and
EuroNCAP test results was then evaluated for vehicles tested under the EuroNCAP test program prior to the
commencement of the study. In addition, the correlation between EuroNCAP protocol test results and injury
outcome in real crash data from Australia and New Zealand was investigated.

SARAC II sub-task 2.2 extends the analysis of subtask 2.1 by focusing on front impact and side impact
police reported crashes. This sub-task aims to evaluate the relationship between EuroNCAP test results and
injury outcome in police reported crashes for each of these crash types in Great Britain, France and Australia
and New Zealand. Crash configuration information was unavailable in sufficient detail to enable similar
analysis of the German data.

Data Sources

EuroNCAP Test Results

The EuroNCAP Executive supplied EuroNCAP data for use in this study covering all tests completed up to
mid 2003.    Results supplied from the EuroNCAP program covered the three main test procedures
comprising the program. These were the 64km/h 40% offset barrier test, the 50km/h side impact test using
950kg mobile barrier and the pedestrian impact test incorporating leg form to bumper test and head form to
bonnet test. Where conducted, the results of the side impact pole test were also supplied. For details of the
pole test, see EuroNCAP (2003). Full details of the other EuroNCAP test procedures and protocols are
described in Williams (1997). Test results were available for a total of 138 different vehicle models. This is
an increase of 73 vehicle models from the time of the pilot study (SARAC I).

Australian ANCAP Test Results

Since 1999, the ANCAP program has adopted a vehicle test and scoring procedure fully harmonised with the
EuroNCAP program. The ANCAP data for use in this study using the EuroNCAP protocol was supplied by
Michael Paine of Vehicle Design and Research Australia with permission of the Australian NCAP Program
Steering Committee. As for EuroNCAP, the ANCAP program uses three main test procedures for vehicle
occupant and pedestrian protection assessment; the 64km/h 40% offset barrier test, the 50km/h side impact




                                                                                                           71
                                                         Relationship between EuroNCAP Results and Real
CEA/EC SARAC II
                                                                                        World Crashes


test using 950kg mobile barrier and the pedestrian impact test incorporating leg form to bumper test and
head form to bonnet test. Where a suitable head protection device is incorporated in the vehicle and at the
request of the manufacture, a side impact pole test is also carried out. Reflecting the harmonisation of the
programs, scoring of the test outcomes in ANCAP is also identical to EuroNCAP.

British Real Crash Data

The STATS19 database covering all crashes in Great Britain reported to Police over the period 1993 to 1998
was supplied by the UK Department for Transport (DfT – formerly the Department of Environment,
Transport, and the Regions) for use in the pilot study conducted in SARAC I. Full details of that data are
provided in Newstead et al, 2001. Additional data, for use in the current project, sub-tasks 2.1 and 2.2,
covering police reported crashes in Great Britain for the period 1999 to 2001 was provided by the UK
Department for Transport (DfT) in the same format as the earlier data.

Generally, only crashes involving injury are reported to police in Great Britain. Considering the combined
data set from 1993 to 2001, and after selecting passenger cars only, complete information for the required
variables (driver age, driver sex, junction type, point of impact and speed limit of the crash site) was available
for 1,635,296 crashes. Estimation of injury risk using the DfT and Newstead methods considered 973,613
and 546,984 two-car crashes respectively. A total of 775,972 injured drivers were available for analysis of
which 159,306 were involved in single vehicle crashes and 616,666 were involved in two-car crashes.

Crashed vehicles with primary impact to specific areas of the vehicle could be identified in the British data
using the “1st Point of Impact” variable in the vehicle section of the database. Selecting from the final data
set described above, 551,841 and 383,033 crashes were available for use in the estimation of driver injury
risk for front impact crashes using the DfT and Newstead methods respectively. Estimation of the injury
severity measure for front impact crashes involved the analysis of 411,691 cases. For side impact crashes
129,639 and 66,198 crashes were available for use in the estimation of driver injury risk using the DfT and
Newstead methods respectively. Injury severity was estimated from 137,433 injured drivers.

Vehicle models for comparison with EuroNCAP test results were identified in the British crash data through
use of the detailed make and model codes appearing in the British data.

French Real Crash Data

In France, every road accident in which at least one road user received medical treatment is investigated by
the police and included in a national database managed by the Ministry of Transportation. The Laboratory of
Accidentology and Biomechanics PSA (LAB) in France supplied an extract of the data for use in this project.
The data covered accidents occurring from 1993 to 2001 not involving a two-wheeler or pedestrian and
including drivers or right front passengers of private cars whose injury outcome is known. Considering the
final data set for crashes occurring between 1993 and 2001, 610,118 two-car and single vehicle crashes
were identified that contained complete information concerning the variables required for analysis.
Estimation of injury risk using the DfT and Newstead methods considered 424,753 and 280,603 two-car
crashes respectively. Estimation of injury severity using the MUARC severity measure considered a total of



72
                                                        Relationship between EuroNCAP Results and Real
CEA/EC SARAC II
                                                                                       World Crashes


379,557 injured drivers of which 98,249 were involved in single vehicle crashes and 281,308 were involved
in two-car crashes.

Crashed vehicles with primary impact to specific areas of the vehicle could be identified in the French data
using the “Point of Initial Impact” variable in the database. Selecting from the final data set described above,
312,945 and 224,732 crashes were available for use in the estimation of driver injury risk for front impact
crashes using the DfT and Newstead methods respectively. Estimation of the injury severity measure for
front impact crashes involved the analysis of 272,965 cases. For side impact crashes 35,297 and 17,792
crashes were available for use in the estimation of driver injury risk using the DfT and Newstead methods
respectively. Injury severity was estimated from 33,253 cases in which driver injury was sustained.

Vehicle models for comparison with EuroNCAP test results were identified in the French crash data through
use of the available make and model codes appearing in the data. The data supplied for SARAC I (1993-
1998) contained only broad vehicle model classifications. However, the more recent data contains sufficient
detail to enable the identification of equivalent EuroNCAP tested models in the French data with a precision
much closer to that available when using the British data.

German Real Crash Data

In Germany, every road accident attended by the police must be reported and is recorded in a database held
at the German Federal Statistical Office. There are no strict injury criteria for inclusion in the database and
accidents involving material damage or slight personal injuries are included where the accident was reported
to the police. A copy of this database for the period 1998 to 2002 was supplied to MUARC for use in this
study.

Considering the complete data set for crashes occurring between 1998 and 2002, 804,589 two-car and
single vehicle crashes were identified and contained complete information concerning the variables required
for analysis. Estimation of injury risk using the DfT and Newstead methods considered 364,939 and 221,132
two-car crashes respectively. Estimation of injury severity considered a total of 273,421 injured drivers
involved in either single vehicle or two-car crashes. Information on the primary point of impact on the
vehicles was not sufficient to identify front and side impact crashes with certainty. Therefore, analysis of
these crash types could not be conducted using the German data.

Vehicle models for comparison with EuroNCAP test results were identified in the German crash data using a
method developed by the BAST on the basis of the “HSN” and “TSN” variables describing vehicle make and
model that were available in the data.

Finnish Real Crash Data

Finnish insurance data was supplied for use in this study by Helsinki University of Technology. However,
there was insufficient data to enable the estimation of vehicle safety ratings with sufficient accuracy for
meaningful analysis. .




                                                                                                             73
                                                         Relationship between EuroNCAP Results and Real
CEA/EC SARAC II
                                                                                        World Crashes


Australian and New Zealand Real Crash Data

Data from four states of Australia and the whole of New Zealand were combined to produce the Australia
and New Zealand make and model specific crashworthiness ratings of Newstead et al (2004). The ratings
covered drivers of cars, station wagons, four-wheel drive vehicles, passenger vans, and light commercial
vehicles manufactured during 1982-2002 and crashing in the Australian states of Victoria and New South
Wales during 1987-2002 or the Australian states Queensland and Western Australia during 1991-2002 and
in New Zealand during 1991-2002.

Estimation of injury risk using the MUARC method considered 1,070,369 crashes that had complete
information for the required variables. Estimation of injury severity using the MUARC severity measure
considered 251,269 drivers injured in a crash during 1987-2002. Selecting from the final data set described
above, 140,184 crashes were available for use in the estimation of driver injury risk for front impact crashes
using the MUARC method. Estimation of the injury severity measure for front impact crashes involved the
analysis of 75,478 cases. Injury risk in side impact crashes was estimated using 15,605 cases whilst injury
severity in these crashes was estimated from 11,459 injured drivers.

Vehicle model details in the Australian and New Zealand data were identified using a process of VIN
decoding.

Comparison of the European Data Sets

There were a number of fundamental differences between the French, British and German data sources, the
most important of which is the segregation of injury levels coded in the reported data. The British data
divides injured occupants into those severely injured (hospital admissions and other serious outcomes) and
those with minor injuries. In the French data, injured occupants are classified into two groups defined as
those staying less than 7 days in hospital and those staying 7 or more days in hospital. Clearly, these injury
definitions are incomparable between the two data systems. Detailed criteria for the classification of injuries
in the German data have not been provided.

Another apparent difference between the British, French and German databases is the comparative number
of vehicle occupants involved in injury crashes that fall into each injury severity level (Table 15).




74
                                                         Relationship between EuroNCAP Results and Real
CEA/EC SARAC II
                                                                                        World Crashes


    Table 15     Comparison of British and French Data Injury Level Codes (All Crash Types)
       British Injury          % of        French Injury             % of      German Injury          % of
           Level             Injured           Level               Injured        Level             Injured
                           Drivers at                            Drivers at                         Drivers
                              Level                                 Level                           at Level
   Fatal (death < 30       0.4           Killed (death <7        3.0                      Killed   0.3
   days after crash)                     days after crash)
   Severely     Injured    4.7           Severely     Injured    11.6          Severely            5.3
   (including       any                  (>6      Days      in                 Injured
   hospital admission                    hospital)
   and other serious
   outcomes)
   Slight         Injury   42.4          Slightly Injured (<7    47.6           Slightly Injured   28.4
   (injured but not                      days in hospital)
   severely)
   Total Injured           47.5          Total Injured           62.2          Total Injured       34
   No Injury               52.5          Uninjured               37.8          Uninjured           66

As a result of the inconsistencies in defining injury severity levels, crash reporting and the differing level of
specificity relating to vehicle model identification in the British, French and German databases, parallel rather
than combined analysis of the three data sources has been conducted. This approach was also adopted in
SARAC 1. Similar outcomes from analysis of the three data sources would serve to confirm the results
obtained whilst differences in analysis outcomes could be investigated in the context of the differences noted
above.

Method
Vehicle Safety Measures Based on Real Crashes
The real crash measure estimated is the risk of serious injury (including death) to a vehicle driver given
involvement in a crash where at least one person was injured.            It is computed as a product of two
components, the first being the risk of driver injury given involvement in an injury crash, the second being a
risk of serious injury given that some level of injury to the driver was sustained. Separate sets of real crash
measures were estimated based on all crash types, frontal impact crashes and crashes to the near (driver's)
side of the vehicle. This approach to representing real crash outcomes has been used successfully in
previous studies correlating real crashes with NCAP-style barrier crash test results from Australia and the
USA.

Two methods of estimating real crash injury risk are used in this study. The first injury risk measure is a
modified version of that used by the DfT to estimate vehicle passive safety ratings in the UK and is based on
the analysis of crashes between two light passenger vehicles. The second measure of injury risk, denoted
the Newstead method, has also been estimated for the three crash groupings considered (all crash types,
front impact and side impact crashes) and is described in detail in the SARAC I sub-task 1.6 and 3.4 project
reports. It stems from considering the same 2-car crash outcomes on which the DfT injury risk measure is
estimated. The injury severity measure is similar to that used by the Monash University Accident Research
Centre in producing vehicle safety ratings in Australia and is based on the analysis of both multi vehicle and
single vehicle crash outcomes. Both components were estimated using logistic regression analysis, adjusting




                                                                                                               75
                                                         Relationship between EuroNCAP Results and Real
CEA/EC SARAC II
                                                                                        World Crashes


for the influence of driver sex and age, point of impact on the vehicle, road junction type, and speed limit or
level of urbanisation, along with first and higher order interactions between these factors.       In addition,
estimates of injury severity were adjusted for the number of vehicles involved in the crash. When the two
components were multiplied, they represented the risk of serious injury to drivers, a measure commonly
used internationally for rating cars in terms of their crashworthiness.

Methods of Comparing Real Crash Injury Measures with EuroNCAP Scores

Preliminary analysis has focused on examining the average crashworthiness ratings derived from the police
reported data of vehicles within each overall star-rating category assigned by the EuroNCAP test program.
Lie and Tingvall (2000) have used this approach to make basic comparisons of real crash outcomes in
Sweden with EuroNCAP test results. Comparison was made for each crash type considered in the real
crash data with specific comparisons between the frontal crash ratings and the offset frontal EuroNCAP test
results and the side impact crash ratings and side impact test EuroNCAP score.            Previous work has
highlighted the relationship between vehicle mass and real crash outcome, with vehicles of higher mass
generally having better real crash ratings. In contrast, the EuroNCAP score is purported to be independent
of vehicle mass. Therefore, analysis including vehicle mass as an extra predictive term in the logistic
regression has been conducted to remove the effect of vehicle mass from the analysis.

As well as examining the average injury outcome in police reported crashes within each EuroNCAP star
rating, comparisons have also been made on a vehicle by vehicle basis. Comparisons on this basis were
made graphically with the underlying EuroNCAP score from which the overall star ratings is derived plotted
against the crashworthiness ratings calculated from the police reported data. Comparisons have been made
for all crash types as well as for frontal and side impact crashes.

Results

Analysis in this sub-task has generated a large number of results across a number of jurisdictions. These
include estimates of crashworthiness, injury risk and injury severity calculated using two methods of safety
rating. Comparison of the EuroNCAP overall scores, front impact and side impact scores with safety ratings
estimated for all crashes, front impact and side impact crashes only have also been made for both European
and Australasian jurisdictions. The role of vehicle mass in determining the level of association between the
two ratings systems has also been investigated. Given the large volume of results available, only those most
relevant to the aims of the study and those most representative of the true relationship between EuroNCAP
test scores and injury outcome in police crash reports are presented below.

Real Crash Based Ratings for EuroNCAP Tested Vehicle Models

Of the 138 EuroNCAP crash tested vehicle models available for use in this study, there were 70, 54 and 23
vehicles with sufficient British real crash data from all crash types, frontal impact crashes and side impact
crashes respectively to be included in the analysis. To illustrate the contents of the estimated vehicle
ratings, Table 16 below presents the ratings estimated by applying the DfT method to police reported crash
data for all crash types in Great Britain.



76
                                                           Relationship between EuroNCAP Results and Real
CEA/EC SARAC II
                                                                                          World Crashes


Table 16          Estimated vehicle secondary safety ratings estimated using the DfT method and
applied to British police reported crash data for all crash types.

                                      ALL CRASH TYPES (DfT Method)

                                                                                                  CWR
Euro                                Crash-                 Estimated   Lower    Upper
                                             Estimated                                   Range    Coeffi
NCAP Vehicle Make/Model worthiness                           Injury    95% CI   95% CI
                                             Injury Risk                                 of CI   cient of
Index                               Rating                  Severity   CWR      CWR
                                                                                                 Variation

        All Model Average            6.79      63.34         10.72

 1      Fiat Punto 55S               7.68      71.29         10.77      6.53     9.02    2.49      0.32

 2         Ford Fiesta 1.25 LX       7.43      68.39         10.87      6.75     8.18    1.43      0.19
                           16V
 3      Nissan Micra 1.0L           10.22      71.69         14.26      8.91    11.74    2.83      0.28

 4      Renault Clio 1.2RL           8.29      70.40         11.78      5.92    11.61    5.69      0.69

 5      Rover 100                    9.70      71.92         13.48      8.50    11.06    2.57      0.26

 6      Vauxhall Corsa 1.2LS         7.37      65.98         11.16      5.12    10.59    5.47      0.74

 7      Volkswagen Polo 1.4L         7.42      67.96         10.92      6.32     8.72    2.40      0.32

 8      Audi A4 1.8                  6.16      55.06         11.18      4.31     8.81    4.50      0.73

 9      BMW 316i                     6.14      55.35         11.10      5.32     7.10    1.78      0.29

        Citroen    Xantia    1.8i
 10                                  5.62      55.58         10.11      4.57     6.92    2.35      0.42
        Dimension

 11     Ford Mondeo 1.8LX            6.38      60.30         10.57      5.56     7.31    1.75      0.27

        Mercedes            C180
 12                                  3.07      56.92          5.40      1.83     5.15    3.32      1.08
        Classic

 13     Nissan Primera 1.6GX         7.35      62.75         11.71      5.65     9.56    3.92      0.53

 14     Peugeot 406 1.8LX            5.72      54.07         10.57      4.75     6.88    2.12      0.37

 15     Renault Laguna 2.0RT         5.51      60.69          9.07      4.24     7.16    2.92      0.53

 16     Rover 620 Si                 6.05      58.89         10.27      4.79     7.64    2.86      0.47

 17     Saab 900 2.0i                5.96      47.98         12.43      3.42    10.39    6.96      1.17

 18     Vauxhall Vectra 1.8iLS       7.08      59.89         11.82      6.26     8.00    1.75      0.25

        Volkswagen       Passat
 19                                  8.42      55.12         15.27      5.54    12.78    7.24      0.86
        1.6L (LHD)

 20     Audi A3 1.6                  5.84      60.74          9.62      3.45     9.89    6.44      1.10




                                                                                                             77
                                                 Relationship between EuroNCAP Results and Real
CEA/EC SARAC II
                                                                                World Crashes



     Citroen    Xsara      1.4i
21                                6.57   65.69     10.00     4.33     9.98   5.65    0.86
     (LHD)

     Daewoo Lanos 1.4SE
22                                7.66   66.08     11.59     5.28    11.11   5.83    0.76
     (LHD)

23   Fiat Brava 1.4S              6.83   65.84     10.38     5.56     8.40   2.83    0.41

24   Honda Civic 1.4i             9.02   65.04     13.87     7.79    10.46   2.67    0.30

     Hyundai           Accent
25                                9.28   75.40     12.30     6.47    13.30   6.83    0.74
     1.3GLS (LHD)

27   Peugeot 306 1.6GLX           8.36   67.09     12.47     7.31     9.57   2.26    0.27

     Renault         Megane
28                                6.71   66.45     10.09     5.52     8.16   2.64    0.39
     1.6RT (LHD)

     Suzuki Baleno 1.6GLX
29                                7.74   67.51     11.46     4.82    12.42   7.61    0.98
     (LHD)

     Toyota     Corolla    1.3
30                                8.24   65.33     12.62     6.14    11.08   4.95    0.60
     Sportif (LHD)

     Volkswagen Golf 1.4
31                                8.06   64.37     12.53     4.58    14.21   9.63    1.19
     (LHD)

32   Audi A6 2.4 (LHD)            3.53   54.09      6.53     1.61     7.75   6.14    1.74

33   BMW 520i (LHD)               6.46   50.32     12.84     4.42     9.46   5.04    0.78

     Mercedes             E200
34                                6.12   52.83     11.59     3.58    10.47   6.89    1.13
     Classic (LHD)

36   Saab 9-5 2.0 (LHD)           3.77   46.10      8.18     1.44     9.88   8.44    2.24

     Vauxhall        Omega
37                                5.41   59.02      9.17     4.16     7.03   2.87    0.53
     2.0Gl/GLS (LHD)

     Volvo S70 2.0/2.5 10V
38                                6.35   56.02     11.34     2.92    13.83   10.91   1.72
     (LHD)

39   Ford Focus 1.6 (LHD)         6.19   60.86     10.17     5.10     7.52   2.43    0.39

     Mercedes             A140
40                                9.12   66.93     13.63     5.57    14.93   9.36    1.03
     Classic (LHD)

     Vauxhall     Astra 1.6i
41                                7.84   69.55     11.27     6.81     9.03   2.22    0.28
     Envoy

42   Ford Escort 1.6 LX           7.33   66.29     11.05     6.71     7.99   1.28    0.17




78
                                                Relationship between EuroNCAP Results and Real
CEA/EC SARAC II
                                                                               World Crashes


43   Nissan Almera 1.4GX        6.50    66.36      9.80     4.41     9.59   5.18    0.80

     Nissan     Serena    1.6
47                              8.56    63.51     13.49     4.43    16.56   12.13   1.42
     (LHD)

     Volkswagen      Sharan
48                              5.43    54.25     10.00     2.65    11.09   8.43    1.55
     TDI (LHD)

     Vauxhall    Corsa    1.0
56                              7.41    69.69     10.64     6.20     8.86   2.66    0.36
     12v Club

59   Honda Accord 1.8iLS        1.89    63.20      2.99     0.49     7.34   6.86    3.63

61   Saab 9-3 2.0 (LHD)         4.94    45.78     10.79     2.39    10.21   7.82    1.58

63   Ford Ka 1.3 (LHD)          7.81    68.86     11.34     6.62     9.20   2.58    0.33

64   Volvo S40 1.8              4.38    62.10      7.06     2.58     7.44   4.87    1.11

65   Toyota Avensis 1.6S        5.83    63.46      9.18     4.15     8.18   4.03    0.69

     Citroen Saxo 1.1 SX
66                              8.03    75.58     10.63     7.07     9.12   2.05    0.26
     (LHD)

67   Daewoo      Matiz   SE+    13.75   78.38     17.54     9.99    18.92   8.93    0.65
     RHD
69   Fiat Seicento              8.98    76.30     11.77     5.80    13.90   8.10    0.90

70   Ford Fiesta 1.25 Zetec     8.01    70.72     11.32     6.53     9.82   3.29    0.41

     Nissan Micra L 1.0
71                              11.26   73.04     15.41     7.25    17.47   10.22   0.91
     (RHD)

     Peugeot 206 1.3 XR
72                              7.54    69.60     10.83     5.85     9.71   3.87    0.51
     Presence (LHD)

     Renault Clio 1.2 RTE
73                              5.87    68.76      8.53     4.35     7.91   3.57    0.61
     (LHD)

74   Rover 25 1.4i (RHD)        7.66    72.72     10.53     5.06    11.57   6.51    0.85

     Toyota Yaris 1.0 Terra
77                              8.03    71.99     11.16     5.09    12.67   7.58    0.94
     (LHD)

     Volkswagen Polo 1.4
78                              7.35    67.11     10.95     4.81    11.23   6.42    0.87
     (LHD)

81   Nissan Almera Hatch        5.72    64.02      8.93     3.04    10.75   7.70    1.35

84   BMW 316i (LHD)             6.20    61.30     10.11     4.51     8.50   3.99    0.64

89   Peugeot 406 (LHD)          6.42    56.03     11.46     4.60     8.97   4.38    0.68




                                                                                           79
                                                           Relationship between EuroNCAP Results and Real
CEA/EC SARAC II
                                                                                          World Crashes



 91   Rover 75 1.8 (RHD)          3.85        48.05           8.02     1.63      9.13     7.50      1.95

      Vauxhall/Opel    Vectra
 93                               7.64        58.52          13.05     6.08      9.60     3.52      0.46
      1.8 (LHD)

      Volkswagon       Passat
 94                               5.45        56.43           9.66     3.72      7.99     4.28      0.78
      1.9 Tdi (LHD)

      Citroen    Picasso   1.6
 96                               6.65        61.45          10.82     2.89      15.31    12.42     1.87
      LX (LHD)

      Renault     Scenic   1.4
102                               5.97        66.54           8.98     3.38      10.55    7.17      1.20
      (LHD)

112   Mazda MX-5 1.6 LHD          9.33        66.41          14.05     6.27      13.90    7.63      0.82

      Jeep Cherokee 2.5 TD
115                               4.20        41.66          10.09     2.12      8.36     6.24      1.48
      Limited (LHD)

      Vauxhall/Opel    Corsa
136                               6.55        65.53           9.99     3.48      12.31    8.82      1.35
      1.2 Comfort (LHD)

Considering the French real crash data, there were 36, 31 and 5 vehicle models with sufficient real crash
data from all crash types, frontal impact crashes and side impact crashes respectively to be included in the
analysis. The German crash data supplied provided sufficient data to estimate ratings for the performance of
53 vehicles across all crash types. There was insufficient point of impact information in the German data to
enable estimation of ratings for front or side impact crashes only. Finally, there was sufficient Australian and
New Zealand real crash data from all crash types, frontal impact crashes and side impact crashes to
estimate ratings for 35, 17 and 6 vehicles respectively.

Comparison of average real crash safety ratings and overall euroncap star ratings
Logistic Regression Analysis

In this study the overall EuroNCAP score and corresponding star rating are calculated based on the driver
dummy measurements in the EuroNCAP test only to ensure compatibility with the real crash ratings that
relate to driver injury outcome only. Average real crash outcomes in all crash types have been estimated
within each EuroNCAP star rating category in each of the European jurisdictions and Australia and New
Zealand and for each of the real crash outcome measures. Table 17 shows average crashworthiness for all
vehicle models within each EuroNCAP overall star rating category with sufficient real data to be included in
the German analysis.




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Table 17         Crashworthiness estimates (DfT method) and 95% confidence limits across
EuroNCAP star rating categories both with and without mass adjustment.

                           Crashworthiness Ratings (DfT method)
                             All Crash Types                                All Crash Types
                         (with mass adjustment)                        (without mass adjustment)
                             Overall Star Rating                           Overall Star Rating
                     1          2          3            4          1         2           3         4
      Estimate               12.17%     11.89%      10.08%                  12.70%      12.46%     9.19%
        LCL                  11.81%     11.51%       9.70%                  12.33%      12.08%     8.86%
          UCL                12.54%      12.28%     10.47%                   13.08%    12.86%      9.54%



In the German data the average crashworthiness for the 4 star rated cars is significantly better than that of
both the 2 and 3-star rated cars that are not significantly different from each other. Similarly, in the French
results, the average crashworthiness of 3 and 4 star rated cars are both statistically significantly lower than
that of 2 star rated cars but are not statistically significantly different from each other. The British results
show the average crashworthiness of the 2, 3 and 4 star rated vehicles is significantly better than the one
star rated vehicle with four star vehicles having the best average crashworthiness.

Trends in average crashworthiness and its component measures by EuroNCAP overall star rating derived
from the Australian and New Zealand crash data were very different from those measured using the
European data sources. No association between average crashworthiness, injury risk or injury severity and
EuroNCAP overall star ratings was observed in the Australian and New Zealand data comparisons. There
are a number of possible causes for the different outcomes in the Australian and New Zealand analysis,
however the exact reasons for the differences are difficult to isolate.

Graphical Analysis

Figure 18 below shows overall EuroNCAP scores plotted against crashworthiness estimated from all crash
types in the German data. Plotting overall EuroNCAP scores against estimated crashworthiness, injury risk
and injury severity using data from the other jurisdictions considered in this study demonstrated very similar
trends.




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                                                                                                    Relationship between EuroNCAP Results and Real
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                                               30.00%




                                               25.00%
       Adjusted Crashworthiness (DfT method)




                                               20.00%




                                               15.00%




                                               10.00%




                                               5.00%



                                                                1 Star                2 Star                      3 Star                   4 Star
                                               0.00%
                                                        0   2     4      6   8   10     12     14     16     18      20     22   24   26     28       30   32

                                                                                         EuroNCAP Overall Test Score


Figure 17                                               Overall EuroNCAP test score vs. German real crash crashworthiness based on all crash
types (DfT Method)

There is evidence of significant differences in the police reported crash measures between vehicle models
within the same EuroNCAP star rating and between vehicle models with almost the same overall EuroNCAP
rating score from which the star ratings are derived.                                                      This is demonstrated by the non-overlapping
confidence limits on the police reported crash measures between pairs of vehicles within the same overall
star rating category. These results are consistent across the three European jurisdictions examined and
across all measures of injury outcome based on police reported crash data.

This result suggests there are other factors, apart from those summarised in the overall EuroNCAP score
that are determining injury outcomes as reported by police. These other factors are also different from those
that have already been compensated for in the estimation of the police reported crash based ratings, such as
driver age and sex and speed limit at the crash location.

Whilst differences exist in the results by jurisdiction and according to the real crash measure being
considered, analysis of the European data sources tends to support some common conclusions when
examining average real crash outcome by EuroNCAP star rating.                                                              Results from each country point to
improving average vehicle crashworthiness with increasing EuroNCAP star rating.                                                                     Analysis of the
component measures of the crashworthiness metric shows this result stems from an association between
average injury severity and overall EuroNCAP star rating and not the injury risk component of the
crashworthiness measure. However, there remains significant variation in the measures of injury outcome in
real crashes for specific vehicles within each EuroNCAP score category. Therefore, a vehicle with a low
crashworthiness or injury severity estimate does not always perform well in EuroNCAP testing and vice
versa. This observation is consistent across the results for all countries considered in the study.


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Results by Crash Configuration

Due to a lack of information on the point of vehicle impact in the German data, no ratings for specific impact
types could be calculated from this data. Subsequently, comparisons by specific crash configurations are
focused on the frontal impact results for the French and British data and the side impact results for the British
data only.

Comparison of average crashworthiness ratings based on frontal impact crashes within EuroNCAP offset
frontal impact star rating categories showed no trends.         This was the case when examining either the
average crashworthiness rating or its injury risk or injury severity components. For illustrative purposes this is
shown in Table 18 below for estimates of crashworthiness based on British data. Similar results were
achieved using French data.

Table 18        Average frontal impact crashworthiness and 95% confidence limits by EuroNCAP frontal
impact star rating categories: with and without mass adjustment

                           Crashworthiness Ratings (DfT method)
                              Front Impact Crashes                      Front Impact Crashes
                             (with mass adjustment)                   (without mass adjustment)
                            Front Impact Star Rating                     Front Impact Star Rating
                          1        2         3         4              1         2         3         4
     Estimate          7.30%    7.45%     7.63%     7.71%          7.46%     7.91%     7.31%     7.41%
     LCL               6.99%    7.15%     7.26%     7.18%          7.14%     7.61%     6.96%     6.91%
     UCL               7.63%      7.77%      8.02%      8.27%      7.79%     8.23%      7.68%      7.96%



Overall, these results suggest there is little if any association between the results of the EuroNCAP offset
frontal impact test and injury outcomes to drivers in frontal crashes reported to police as measured by
crashworthiness estimated using the DfT and Newstead methods.

In contrast to the frontal impact test, a strong association between average crashworthiness in side impact
crashes and the side impact EuroNCAP score was observed in the British data (Table 19). There were
relatively few vehicle models with sufficient side impact data to be reliably rated in the French and Australian
and New Zealand data and to be meaningfully analysed against EuroNCAP frontal and side impact scores.




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                                                                                                            Relationship between EuroNCAP Results and Real
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Table 19                                                  Average side impact crashworthiness and 95% confidence limits by EuroNCAP side
impact star rating categories: with and without mass adjustment

                                                                          Crashworthiness Ratings (DfT method)
                                                                           Side Impact Crashes                                 Side Impact Crashes
                                                                          (with mass adjustment)                            (without mass adjustment)
                                                                          Side Impact Star Rating                                Side Impact Star Rating
                                                                  1              2         3         4                  1              2         3                 4
     Estimate                                                                10.68%     9.09%     6.89%                              10.81%      9.14%            6.77%
     LCL                                                                      9.33%     8.20%     5.80%                                9.45%     8.25%            5.71%
     UCL                                                        0.00%           12.20%       10.06%        8.15%    0.00%            12.33%        10.11%         8.00%



Interpreting the point estimates of the analysis revealed an approximate 20% drop in average side impact
serious injury risk measured from the police reported data with every increase in EuroNCAP side impact star
rating category. Analysis of results shows the association with the side impact crashworthiness rating stems
largely from the association between average side impact injury severity and side impact EuroNCAP rating.
However, comparisons between side impact crashworthiness ratings and side impact EuroNCAP scores on
a vehicle by vehicle basis shows significant dispersion suggesting that a high EuroNCAP score is not
associated with good side impact crashworthiness and vice versa for all vehicle models (Figure 19).




                                              18.00


                                              16.00
      Adjusted Crashworthiness (DfT method)




                                              14.00


                                              12.00


                                              10.00


                                               8.00


                                               6.00


                                               4.00


                                               2.00

                                                                 1 Star                       2 Star                    3 Star                      4 Star
                                               0.00
                                                      0     1      2        3      4     5       6     7      8     9       10     11   12    13      14     15     16

                                                                                                 EuroNCAP Overall Test Score


Figure 18                                                 Side Impact EuroNCAP test score v Adjusted side impact crashworthiness estimated using
British data (DfT method)




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                                                        Relationship between EuroNCAP Results and Real
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Discussion

In many aspects, the results of this study hold many similarities to the results of the Pilot study of Newstead
et al (2001) carried out under Phase I of the SARAC research program. However, in comparison to Phase I
of the SARAC research program, this study is based on much larger quantities of police reported crash data
from a wider range of countries with results based on the analysis of up to 70 EuroNCAP tested vehicle
models.    As such this study provides a much more definitive assessment of the relationship between
EuroNCAP test scores and injury outcomes recorded in police reported crash data. The results of this study
are also consistent with results of other similar studies comparing real crash outcomes and the results of
crash barrier test programs conducted world-wide.

In drawing conclusions from this type of analysis it is interesting to revisit the philosophy of the EuroNCAP
program.   According to those involved in EuroNCAP, the principal purpose of the program is to apply
pressure to vehicle manufacturers to improve the safety design and specification of vehicles. Reflecting the
aims of the program, the scoring system for EuroNCAP is not designed to necessarily represent an injury
risk outcome scale. Instead, the various test measurements are weighted according to how highly it is
desired to influence manufacturers on each aspect of vehicle design.           Recognising the nature of the
EuroNCAP scoring process, a linear relationship between injury outcomes in real world crashes and the
EuroNCAP score would not necessarily be expected. However, given the aim of EuroNCAP is to improve
vehicle safety generally, a general association between improving crashworthiness and higher EuroNCAP
scores would be expected. Considering the analysis of real crash outcomes as the most suitable way of
assessing the effectiveness of the EuroNCAP program in meeting its aims, results of this study confirm this
general association with average real crash outcomes being better in vehicles with higher EuroNCAP scores
than in ones with low scores. Results also confirm that this association is non-linear as expected.

Interpreted in this way, results of analysis in this study confirm that the design priorities for vehicle safety
encouraged by the EuroNCAP scoring process are leading to improved real world crash performance on
average.   Importantly, comparison of the French and British analysis results in particular, suggest that
improvement is greatest in the higher severity real world crashes. However, the results of comparison on a
vehicle by vehicle basis also show that achieving these design priorities does not always lead to a safer
vehicle. This result suggests that EuroNCAP is not necessarily encapsulating all the factors required to
ensure good safety performance in a vehicle.         Alternately, it is allowing vehicles to score well on a
combination of factors that have relatively low effectiveness in improving real world safety. Whether the
EuroNCAP test process can or should be modified to overcome this to some degree remains to be
determined.

A lack of absolute consistency between EuroNCAP ratings and crashes based on real world data on a
vehicle by vehicle basis is only problematic if ratings from the two systems are presented side by side for
consumer information. Fortunately this is rarely possible because of the nature of the ratings. Ratings based
on real world data typically lag those published by EuroNCAP by many years as real world crash experience
accumulates by which time the EuroNCAP test protocol has often been modified and is not directly
comparable.



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As noted, EuroNCAP is seen as a tool for driving safety change in vehicle design and providing information
to consumers on relative safety at the time of vehicle release. In contrast, vehicle safety ratings based on
real world data are seen as a tool to evaluate the long term safety of vehicles in the full range of real world
circumstances.    As shown by this study, real world ratings also provide a means to assess whether
EuroNCAP testing is achieving its stated aims in improving vehicle safety and to help fine tune the program
in future. Viewed as such, both ratings systems have a defined and non-conflicting role in advancing vehicle
safety.

Conclusions

This study has been able to quantify the relationship between injury outcomes in real world crashes reported
to police and estimates of relative vehicle safety derived from the EuroNCAP vehicle crash barrier test
program. The measure of real world injury outcome used has been the risk of death or serious injury given
crash involvement calculated as a product of the risk of injury given crash involvement and the risk of death
or serious injury given an injury was sustained. The crashworthiness measure, as well as its component risk
measures based on all crash configurations, has been compared with the overall EuroNCAP score. Real
world crash outcomes for frontal and driver side impacts have also been compared with the EuroNCAP
offset and side impact test component scores.       Police reported crash data from Great Britain, France,
Germany, Finland, Australia and New Zealand was analysed. Due to the much larger quantities of real world
data available for analysis, up to 70 EuroNCAP tested vehicle models have been considered in the
comparisons meaning results from this study are more definitive than those obtained in the preceding
SARAC 1 pilot study.

Results of analysis of the European data sources support some common conclusions when examining
average real crash outcome by EuroNCAP star rating. Results from each country point to improving average
vehicle crashworthiness with increasing EuroNCAP star rating. Analysis of the component measures of the
crashworthiness metric shows this result stems from an association between average injury severity and
overall EuroNCAP star rating and not the injury risk component of the crashworthiness measure. Measured
associations between EuroNCAP score and real world injury severity were strongest and most consistent in
both the French and German data. The French data in particular uses a much higher severity definition for
serious injury compared to the British data, requiring drivers to be hospitalised for more than 6 days. The
strong association between the French definition and EuroNCAP results suggests EuroNCAP may be
reflecting the likelihood of these more serious injury outcomes.

No association between average crashworthiness, injury risk or injury severity and EuroNCAP overall star
ratings was observed in the Australian and New Zealand data comparisons. This may have been a result of
fewer vehicles being available for analysis, the range of vehicle models analysed being vastly different to
those represented in the European data sources, differences in the injury outcome coding in the Australasian
data or a combination of all these factors

Examination of the relationship between overall EuroNCAP test score and injury outcome on an individual
vehicle basis adds a further dimension to the interpretation of the relationship. They show that whilst there is


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                                                         Relationship between EuroNCAP Results and Real
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and association between average vehicle crashworthiness and EuroNCAP score outcome, there is
significant variation in the measures of injury outcome in real crashes for specific vehicles within each
EuroNCAP score category. It shows that a vehicle with good average real world crash outcomes does not
always perform well in EuroNCAP testing and vice versa. This observation is consistent across the results
for all countries considered in the study.

Comparison of average crashworthiness ratings based on frontal impact crashes within EuroNCAP offset
frontal impact star rating categories showed no trends. The results suggest there is little if any association
between the results of the EuroNCAP offset frontal impact test and real world injury outcomes to drivers in
frontal crashes. In contrast, a strong association between average crashworthiness in side impact crashes
and the side impact EuroNCAP score was observed.             Interpreting the point estimates of the analysis
revealed an approximate 20% drop in average side impact serious injury risk measured from the police
reported data with every increase in EuroNCAP side impact star rating category. Like the comparisons
based on all crash types, comparisons between side impact crashworthiness ratings and side impact
EuroNCAP scores on a vehicle by vehicle basis showed ratings were not always consistent on a vehicle by
vehicle basis. The results of this study are consistent with the results of the Pilot study carried out under
Phase I of the SARAC research program and other similar studies comparing real crash outcomes and the
results of crash barrier test programs conducted world-wide.

EuroNCAP’s principal aim is to apply pressure to vehicle manufacturers to improve the safety design and
specification of vehicles.   Leverage to achieve this end is gained by publishing the results for broad
consumer scrutiny. Reflecting the aims of the program, the scoring system for EuroNCAP is not designed to
necessarily represent an injury risk outcome scale. Results of this study confirm this general association
with average real crash outcomes being better in vehicles with higher EuroNCAP scores than in ones with
low scores. Results also confirm that this association is non-linear as expected. As such the study confirms
that the design priorities for vehicle safety encouraged by the EuroNCAP scoring process are leading to
improved real world crash performance on average. However, the results of comparison on a vehicle by
vehicle basis also show that achieving these design priorities does not always lead to a safer vehicle.

Finally, this study shows that comparison with real world ratings provide a means to assess whether
EuroNCAP testing is achieving its stated aims in improving vehicle safety and to help fine tune the program
in the future. Noting their respective strengths, both EuroNCAP and real world ratings systems have defined
and non-conflicting roles in advancing vehicle safety.

Future Research Directions

The work completed in this sub-task of the SARAC 2 project and detailed in this report has pointed to a
number of areas of future research that should be considered. They are as follows.

•   The EuroNCAP test program is constantly evolving to encourage manufacturers to meet more rigorous
    standards of vehicle safety performance and to include the latest safety technology. These evolutionary
    changes to EuroNCAP need to be evaluated specifically to ensure they are effective in improving
    average vehicle safety in real world crashes.        Periodic evaluation of EuroNCAP using the general



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                                                        Relationship between EuroNCAP Results and Real
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     approach taken in this study is recommended and considered vital to ensure this high profile program
     continues to meet its target of improving vehicle safety performance.

•    One of the limitations of the research presented in this report was the inability to combine the data from
     each of the jurisdictions for combined analysis. It is recommended that research be undertaken to
     investigate establishing a standardised European crash data recording protocol. Part of the research
     should investigate the most suitable measure of severe injury outcome (for example hospital admission)
     that can be accurately and consistently coded by police.

•    More in-depth comparisons of the relationship between real world crash outcomes and EuroNCAP test
     scores would have been possible if a greater range of injury severity measures were available than just
     those recorded in the police data.     It is recommended that research be conducted in Europe on
     investigating the availability of other injury outcome data such as insurance claims data and hospital
     records and the potential for linking these records with police crash data reports on a wide scale. The
     resulting combined data would also be a powerful resource for a broad range of detailed vehicle safety
     research in Europe.


6.2       Extended Analysis of Car Model Scores in German Real World
          Data (ST 2.1/2.2 – 2)
SARAC II sub-tasks 2.1 and 2.2 are aimed at generating estimates of real world vehicle crashworthiness
based on Police reported crash data and assessing the relationship between such measures and crash
barrier test results in Europe and Australia. The analysis was conducted using data from Great Britain,
France, Germany and Australia and New Zealand and crash barrier test results from the EuroNCAP and
ANCAP testing programs. The results of the analysis are reported in SR-248 and differ across jurisdictions.
Both the French and German results show consistent trends in improving crashworthiness with increasing
EuroNCAP star rating across the 2, 3 and 4 star rated cars available for comparison. However, the statistical
significance of the observed trends in these countries differs slightly. The British results show the average
crashworthiness of the 2, 3, and 4 star rated vehicles is significantly better than the one star rated vehicle,
with four star rated vehicles having the best average crashworthiness. However, no significant difference or
clear trend in the average crashworthiness of 2 and 3 star rated cars was seen in the British data.

The lack of conclusive and consistent evidence of improving crashworthiness with increasing EuroNCAP star
ratings across the vehicles available for comparison has a number of potential causes and it is the aim of this
report to examine one of them. In previous analysis, the matching of vehicles appearing in the mass crash
data sources with vehicles tested under EuroNCAP was as rigorous as possible. Vehicles were selected
from mass crash data for inclusion in the analysis only if they were sufficiently similar to the vehicle model
tested under EuroNCAP. This approach was adopted to ensure that the correlation analysis undertaken
compared the performance of similar vehicles across the two rating methods. However, this limited the
amount of data available for estimation of real world crashworthiness ratings and likely contributed to the
wide confidence limits associated with many of the ratings. It is possible that his approach unnecessarily



88
                                                         Relationship between EuroNCAP Results and Real
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excluded vehicles from the analysis that, if included, may have reduced the size of the confidence limits
associated with the crashworthiness ratings and enabled more definitive analysis of the relationship between
the two safety rating systems.

To investigate whether a re-classification of vehicles in the mass crash data would result in more definitive
conclusions regarding the correlation between the two measures of vehicle safety, a re-analysis of the
German mass crash data was undertaken. The definition of a vehicle type that can reasonably be compared
with a EuroNCAP tested vehicle was broadened using additional information in the German data that
provides the model and generation of crash involved vehicles. The authors are unaware of the source of the
vehicle generation information, however, it has been taken to be an accurate description of the crash
involved vehicle and to define a relatively homogeneous group of vehicles with respect to vehicle structure
and safety specifications.

Data sources

Police reported crash data from Germany for the years 1998 to 2002 was used in this analysis. In Germany,
every road accident attended by the police must be reported and is recorded in a database held at the
German Federal Statistical Office. All crashes reported to police, including those involving only material
damage or slight personal injuries, are included in the database. The sub-set of the data used here is
identical to that used in SARAC II sub-tasks 2.1 and 2.2 and a full description of the data set used is
available in SR-248.    The additional information used to describe the vehicle model and generation is
provided in the “modcnam” variable of the German Police reported crash data. The numerical indicator of
model generation was used to distinguish between vehicle models of the same type but of different
generations with more recent models having higher generation numbers (e.g. VW Golf 1, VW Golf 2). The
re-analysis was undertaken using German data only, as data from the remaining jurisdictions does not
provide information on the model generation of crash involved vehicles and this information could not be
easily derived from the existing data.

Method

Estimates of crashworthiness based on the extended vehicle definitions have been calculated as part of
SARAC sub-task 3.3 (SR-256) using the logistic regression procedure described in detail in SR-248.
Estimation of the injury risk component of the crashworthiness rating was conducted using the DfT method
applied to crash data for crashes involving two passenger vehicles only. The severity component of the
crashworthiness rating was estimated using the MUARC method and used crash data from both single
vehicle and vehicle-to-vehicle crashes. In order to obtain estimates of injury risk and injury severity unbiased
by factors other than vehicle make and model, a number of factors thought to influence the risk and severity
of injury to drivers were included in the logistic regression model. Those factors considered were the same
as those used in sub-tasks 2.1 and 2.2 for the German data.            Table 20 details the main effects and
interactions that were judged to be significant predictors of injury risk and injury severity through the stepwise
logistic modelling approach.




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Table 20        Significant factors in the logistic regression models of injury risk and injury severity
derived from German data using the DETR method.

 Significant Model Factors        All Crash Types (Injury risk)         All Crash Types (Injury Severity)

                                  driver age (age),                     driver age (age),
                                  driver sex (sex),                     driver sex (sex),
                                                                        number of vehicles (nbv),
                                  intersection (int),
                                                                        location of crash (loc),
 Main Effects
                                  location of crash (loc)
                                                                        cost of crash (cost)
                                  cost of crash (cost)
                                                                        year of crash (year)
                                  year of crash (year)
                                                                        age*sex, age*nbv, sex*nbv,
                                  sex*age, age*int, age*loc, int*loc,   age*int, sex*int, veh*int, veh*loc,
 First Order Interactions         age*cost, sex*cost, int*cost,         int*loc, age*cost, sex*cost,
                                  loc*cost                              nbv*cost, int*cost, loc*cost,
                                                                        int*year, loc*year, cost*year
                                                                        age*sex*nbv, nbv*int*loc,
                                                                        age*int*cost, nbv*int*cost,
 Second Order Interactions        int*loc*cost
                                                                        nbv*loc*cost, int*loc*cost,
                                                                        int*cost*year
Of those models for which revised estimates of crashworthiness were calculated in sub-task 3.3, models
were selected for inclusion in the correlation analysis where there was a EuroNCAP tested vehicle that could
be identified as being of the same generation as the subject vehicle. Table 21 shows the 32 vehicle models
selected for inclusion in the analysis.   The vehicle identification index refers to the index assigned to
EuroNCAP tested vehicles in Appendix A of SR-248. The vehicle make and model information is derived
from the ‘mancnam’ and ‘modcnam’ variables in the German Police reported mass crash data. The number
of crash involved and injured drivers for each vehicle model is also provided under both the extended
definition of vehicles that match a EuroNCAP tested vehicle and, where available, under the original
definition used in SR-248.




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                                                       Relationship between EuroNCAP Results and Real
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Table 21        Number of injured and involved drivers of crashed vehicles in the German data from
1998 to 2002 using the extended definition of vehicles that match EuroNCAP tested vehicles.
                                                  Involved       Involved       Injured         Injured
Vehicle                                           Drivers        Drivers        Drivers         Drivers
Identificatio Vehicle Make/Model                  (Extended      (Original      (Extended       (Original
n Index                                           vehicle        vehicle        vehicle         vehicle
                                                  definition)    definition)    definition)     definition)
     8       AUDI AG A4-1                             4217            1189           2266            773
     83      AUDI AG A4-2                              315                           152
     7       VOLKSWAGEN-VW Polo-3                     5986            1865           4289           1791
     9       B M W 3er-3                              7416             643           4348           547
     33      B M W 5er-4                              2239             530           951            317
     51      CHRYSLER (USA) Voyager-2                  297             116           121             61
     1       FIAT (I) Punto                           3175            1814           2420           1622
     52      FIAT (I) Punto-2                          480             347           339            291
     42      FORD/EUROPA Escort-4                     8390            1973           5172           1535
     2       FORD/EUROPA Fiesta-3                     9213                           6923
     70      FORD/EUROPA Fiesta-4                     3003                           2069
     11      FORD/EUROPA Mondeo                       3787             640           2042            394
    138      FORD/EUROPA Mondeo-2                      185                            83
     12      MERCEDES BENZ AG C-1                     5024            2254           2594           1509
     59      HONDA MOTOR (J) Accord-5                  101                            51
     24      HONDA MOTOR (J) Civic-6                   488             740           298             690
     34      MERCEDES BENZ AG E-5                     3344             378           1648            221
     43      NISSAN (J) Almera                         780             575           470             452
     81      NISSAN (J) Almera-2                       109                            66
     3       NISSAN (J) Micra-2                       1765            1549           1395           1475
     13      NISSAN (J) Primera-2                      593             248           340            189
     41      OPEL Astra-2                             3192            1914           1865           1426
     6       OPEL Corsa-2                             9178            3306           6828           3055
    136      OPEL Corsa-3                              364             130           248            121
     37      OPEL Omega-2                             2159                           1053
     18      OPEL Vectra-2                            3538            1009           1920            726
     4       RENAULT (F) Clio-1                       3763             408           2661            402
     73      RENAULT (F) Clio-2                        655             334           473             327
     75      SEAT (E) Ibiza-2                         2082                           1434
     30      TOYOTA (J) Corolla-5                      631             440           400            356
     31      VOLKSWAGEN-VW Golf-4                     5829            1709           3543           1266
     19      VOLKSWAGEN-VW Passat-4                   3846             721           1921           451
When comparing the number of crash involved and injured drivers available for analysis for each vehicle
model in this analysis and in the original analysis of the German data, it is evident that by broadening the
definition of vehicles that match EuroNCAP tested cars there is a large increase in the amount of data
available. Of the 24 vehicle models for which crashworthiness estimates were calculated under both the
original and extended vehicle definitions, all but one experienced some increase in the data available for
analysis under the extended definition of vehicles that match EuroNCAP tested cars.            Some vehicles
experienced large increases in the availability of data and an additional eight vehicles had sufficient data to
be included in the revised analysis.




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                                                      Relationship between EuroNCAP Results and Real
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Results

Injury risk, injury severity and crashworthiness ratings for each of the vehicle models considered are
presented in Table 22 below.    Upper and lower confidence limits and confidence limit width for each
estimated crashworthiness rating are also provided. The coefficient of variation shown is the ratio of the
width of the confidence limit to the magnitude of the point estimate and is useful as a scaled measure of
rating accuracy. These ratings are the same as those presented in SR 256.

Table 22       Vehicle safety ratings estimated from German crash data 1998-2002.
                                                                CWR 95% CL
                                                                                       CoV         CoV
Vehicle                                                                         95% CL
              Vehicle Make/Model CWR         Risk     Severity Lower     Upper         (Exten      (Origi
Index                                                                           Width
                                                                                       ded)        nal)
8             AUDI AG A4-1        7.00%      53.39% 13.12%      6.34%    7.74% 1.40% 0.2           0.35
83            AUDI AG A4-2        4.66%      46.83% 9.94%       2.92%    7.42% 4.50% 0.97
7             VOLKSWAGEN-VW 9.77%            67.36% 14.50%      9.07%    10.52% 1.44% 0.15         0.24
              Polo-3
9             BMW 3er-3           7.84%      57.64% 13.60%      7.33%    8.38%    1.05%    0.13    0.42
33            BMW 5er-4           3.98%      40.70% 9.77%       3.38%    4.68%    1.29%    0.33    0.61
51            CHRYSLER (USA) 4.30%           36.45% 11.79%      2.73%    6.77%    4.05%    0.94    1.25
              Voyager-2
1             FIAT (I) Punto      10.91%     73.33% 14.88%      9.88% 12.05% 2.17%         0.2     0.27
52            FIAT (I) Punto-2    7.60%      65.84% 11.55%      5.78% 9.99% 4.21%          0.55    0.7
42            FORD/EUROPA         12.22%     64.06% 19.08%      11.50% 12.99% 1.50%        0.12    0.24
              Escort-4
2             FORD/EUROPA         15.88%     75.84% 20.94%      15.02% 16.79% 1.77%        0.11
              Fiesta-3
70            FORD/EUROPA         10.90%     65.85% 16.55%      9.88%    12.02% 2.14%      0.2
              Fiesta-4
11            FORD/EUROPA         7.75%      55.11% 14.07%      6.98%    8.61%    1.63%    0.21    0.54
              Mondeo
138           FORD/EUROPA         4.36%      43.42% 10.04%      2.50%    7.60%    5.10%    1.17
              Mondeo-2
12            MERCEDES BENZ 5.42%            50.58% 10.72%      4.91%    6.00%    1.09%    0.2     0.28
              AG C-1
59            HONDA MOTOR (J) 5.81%          50.57% 11.49%      3.07%    10.99% 7.91%      1.36
              Accord-5
24            HONDA MOTOR (J) 8.84%          60.14% 14.69%      6.91%    11.30% 4.38%      0.5     0.33
              Civic-6
34            MERCEDES BENZ 5.09%            49.86% 10.20%      4.46%    5.81%    1.35%    0.27    0.74
              AG E-5
43            NISSAN (J) Almera 9.18%        58.71% 15.64%      7.57%    11.13% 3.56% 0.39         0.44
81            NISSAN (J) Almera-2 10.31%     54.94% 18.76%      6.42%    16.54% 10.11% 0.98

3             NISSAN (J) Micra-2 15.38%      75.82% 20.28%      13.74% 17.22% 3.48%        0.23    0.24
13            NISSAN (J) Primera- 6.78%      56.62% 11.98%      5.30% 8.68% 3.37%          0.5     0.7
              2
41            OPEL Astra-2        6.69%      56.62%   11.82%    5.98%    7.50%    1.52%    0.23    0.28
6             OPEL Corsa-2        11.62%     71.21%   16.32%    10.95%   12.34%   1.40%    0.12    0.2
136           OPEL Corsa-3        7.73%      61.89%   12.49%    5.78%    10.35%   4.57%    0.59    0.83
37            OPEL Omega-2        5.11%      49.23%   10.39%    4.37%    5.99%    1.62%    0.32


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18             OPEL Vectra-2       7.20%      54.67%   13.17%     6.48%     8.00%    1.52%   0.21     0.38
4              RENAULT (F) Clio-1 14.94%      69.56%   21.47%     13.87%    16.09%   2.22%   0.15     0.45
73             RENAULT (F) Clio-2 10.99%      67.80%   16.21%     9.14%     13.21%   4.07%   0.37     0.5
75             SEAT (E) Ibiza-2    10.69%     66.11%   16.18%     9.57%     11.94%   2.37%   0.22
30             TOYOTA (J) Corolla- 8.17%      61.53%   13.28%     6.51%     10.25%   3.74%   0.46     0.57
               5
31             VOLKSWAGEN-VW 6.44%            59.48% 10.82%       5.86%     7.07%    1.21%   0.19     0.33
               Golf-4
19             VOLKSWAGEN-VW 5.18%            51.43% 10.08%       4.55%     5.91%    1.36%   0.26     0.55
               Passat-4
The average coefficient of variation has also been calculated over the 24 vehicle models included in both the
original and revised analysis. The average coefficient of variation of crashworthiness estimated using the
extended vehicle model definitions is 0.31 compared to 0.48 when estimated using the original vehicle model
definitions. This shows that by increasing the data available for analysis the accuracy of the ratings has
improved.   This should be reflected in a reduced width of the confidence limits associated with the
crashworthiness estimates.

Comparison of real crash safety ratings and overall EuroNCAP star ratings

Given that broadening the definition of vehicles that match EuroNCAP tested vehicles results in more precise
crashworthiness ratings on average, a comparison of the revised real crash safety ratings and overall
EuroNCAP star ratings may result in more definitive conclusions regarding the correlation between the two
measures of vehicle safety.

The following series of figures show overall EuroNCAP scores plotted against crashworthiness, injury risk
and injury severity estimated from all crash types in the German Police reported crash data. Individual
EuroNCAP scores are grouped according to the corresponding star rating and 95 per cent confidence limits
are placed on the estimates of the real crash measures.




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                                                                                                   Relationship between EuroNCAP Results and Real
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                                                                                                                                  World Crashes



                                     20.00%


                                     18.00%


                                     16.00%


                                     14.00%
      DETR Adjusted Crashwortiness




                                     12.00%


                                     10.00%


                                      8.00%


                                      6.00%


                                      4.00%


                                      2.00%

                                                    1 Star               2 Star                            3 Star         4 Star
                                      0.00%
                                              0        4        8          12                 16                20   24     28      32

                                                                                  EuroNCAP Overall Test Score



Figure 19                                         Overall EuroNCAP test score vs. estimated German real crash vehicle crashworthiness
based on all crash types.




                                     90.00%



                                     80.00%



                                     70.00%



                                     60.00%
 DETR Adjusted Injury Risk




                                     50.00%



                                     40.00%



                                     30.00%



                                     20.00%



                                     10.00%


                                                    1 Star                2 Star                            3 Star        4 Star
                                     0.00%
                                              0       4         8          12                16                 20   24    28       32

                                                                                EuroNCAP Overall Test Score



Figure 20                                         Overall EuroNCAP test score vs. estimated German real crash vehicle injury risk based on
all crash types.




94
                                                                                         Relationship between EuroNCAP Results and Real
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                                                                                                                        World Crashes


                             30.00%




                             25.00%




                             20.00%
 DETR Adjusted Injury Risk




                             15.00%




                             10.00%




                             5.00%




                                            1 Star               2 Star                           3 Star        4 Star
                             0.00%
                                      0      4          8          12               16                20   24    28       32

                                                                        EuroNCAP Overall Test Score



Figure 21                                 Overall EuroNCAP test score vs. estimated German real crash vehicle injury severity based
on all crash types.

Figure 20 to 22 show significant variation in the injury measures of each vehicle estimated from Police
reported crash data within each overall EuroNCAP score range. This variation is partly a product of the
estimation error in each of the police reported crash injury measures, particularly for vehicle models with
relatively few records in the crash data, as shown by the 95% confidence limits on the police reported crash
estimates. However, there are significant differences in the police reported crash measures between vehicle
models within the same EuroNCAP star rating (particularly for 2 and 3 star rated cars), and even between
vehicle models with almost the same overall EuroNCAP rating score from which the star ratings are derived.
This is demonstrated by the non-overlapping confidence limits on the police reported crash measures
between pairs of vehicles within the same overall star-rating category.

This preliminary analysis of the relationship between real world crash measures and EuroNCAP test scores
generates results very similar to those produced from the first analysis of German data using the original
definition of vehicles that match EuroNCAP tested cars. In comparing the charts that appear above to those
presented in SR-248, it is evident that the confidence limits associated with the real world safety estimates
are narrower when broad definitions of vehicles matching EuroNCAP tested vehicles are adopted. Rather
than providing increased evidence of correlation between the two vehicle safety measures, this highlights the
statistically significant variation in the estimated real crash injury measures of vehicles within each overall
EuroNCAP score range.

In summary, the above results suggests there are other vehicle related factors, apart from those summarized
in the overall EuroNCAP score, that are determining injury outcomes as reported by police on a vehicle by
vehicle basis. These other factors are also different from those that have already been compensated for in



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                                                       Relationship between EuroNCAP Results and Real
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the estimation of the police reported crash based ratings, such as driver age and sex and speed limit at the
crash location.

Logistic Regression Analysis

The above analysis of the relationship between the overall EuroNCAP vehicle star rating and real crash
based vehicle safety ratings has been able to identify general relationship trends between the two safety
measures.    In order to make more definitive statements about the relationship between the two safety
measures and the statistical significance of the relationship, a logistic regression framework has been used.
Under this framework vehicle safety rating measures derived from real crash data have been modeled as a
function of the EuroNCAP overall star rating. A full description of the method used is provided in Section 4.4
of SR 248.

Given the proven relationship between vehicle mass and the real crash safety measures, it is necessary to
adjust for vehicle mass when exploring the relationship between these measures and EuroNCAP test
scores. To achieve this, vehicle mass is included as an extra predictive term in the logistic regression
(Equation 1 described in SR-248) and operates to remove the effect of mass from the analysis. Analysis
was conducted both with and without compensating for mass effects to determine more precisely the
relationship between real crash measures and EuroNCAP star ratings. The results of the analysis are
presented in the tables that follow. Each of the tables shows the average real crash measure for all vehicle
models within each EuroNCAP overall star-rating category with sufficient real crash data to be considered in
the study. In addition, the 95% confidence limits for each of the estimates are given to allow comparison of
the statistical significance in the average real crash outcome between pairs of EuroNCAP overall star rating
categories. The data presented here considers all crash types only as there is insufficient point of impact
information available in the crash data to enable analysis by crash configuration.

                                DETR Crashworthiness Ratings
                                       All Crash Types                      All Crash Types
                                    (With mass adjustment)            (Without mass adjustment)
                                      Overall Star Rating                 Overall Star Rating
                                  1        2       3       4          1       2        3      4
       Estimate                         10.56% 10.65% 8.02%                11.46% 11.47% 6.81%
       LCL                              10.32% 10.40% 7.75%                11.21% 11.21% 6.59%
       UCL                     0.00% 10.80% 10.90% 8.30%           0.00% 11.71% 11.73% 7.05%



Figure 22         DETR crashworthiness estimates and 95% confidence limits across EuroNCAP star rating
categories both with and without mass adjustment.

Considering both the mass adjusted and non-mass adjusted analysis for crashworthiness, 4 star rated
vehicles have an average crashworthiness significantly better than lower star rated vehicles.     However, 2
star rated vehicles have an estimated average crashworthiness not statistically different to 3 star rated
models. These results are consistent with those established in SR-248.



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                                                                                        World Crashes




                                     DETR Injury Risk Ratings
                              All Crash Types                                 All Crash Types
                         (With mass adjustment)                      (Without mass adjustment)
                           Overall Star Rating                           Overall Star Rating
                1              2         3         4               1        2          3         4
   Estimate               62.20%     62.74%    60.58%                  64.52%      64.81%    55.96%
   LCL                    62.29%     60.01%    61.76%                  64.11%      64.38%    55.40%
   UCL      0.00%         63.19%     61.15%    62.63%          0.00%   64.93%      65.24%    56.52%

Figure 23       DETR injury risk estimates and 95% confidence limits across EuroNCAP star rating
categories both with and without mass adjustment.

With respect to injury risk unadjusted for vehicle mass effects, 4 star rated vehicles have statistically
significantly lower average injury risk than lower star rated vehicles. However, no statistically significant
differences between 2 and 3 star rated vehicles could be detected. Considering the analysis of injury risk
adjusted for vehicle mass effects, it is not possible to statistically significantly differentiate real crash
performance on the basis of EuroNCAP star ratings. These results are very similar to those established in
SR-248 particularly where there is no adjustment for vehicle mass effects.



   DETR Injury Severity Ratings
                              All Crash Types                                 All Crash Types
                         (With mass adjustment)                      (Without mass adjustment)
                           Overall Star Rating                           Overall Star Rating
                1              2         3         4               1        2          3         4
   Estimate               16.61%     16.49%    13.08%                  17.34%      17.16%    11.99%
   LCL                    16.16%     16.02%    12.55%                  16.88%      16.69%    11.52%
   UCL      0.00%         17.08%     16.97%    13.64%          0.00%   17.81%      17.64%    12.48%



Figure 24       DETR injury severity estimates and 95% confidence limits across EuroNCAP star rating
categories both with and without mass adjustment.

Considering both the mass adjusted and non-mass adjusted analysis for injury severity, 4 star rated vehicles
have an average injury severity significantly better than lower star rated vehicles. However, 2 star rated
vehicles have an estimated average injury severity rating not statistically significantly different to 3 star rated
models. These results are identical to those established in SR-248.

Conclusions

The aim of this extended analysis was to examine whether redefining vehicles appearing in German mass
crash data that match EuroNCAP tested vehicles would result in more definitive conclusions regarding the
relationship between crashworthiness measures based on real world crash data and EuroNCAP test scores.
The analysis conducted found that by broadening the definition of vehicles that match EuroNCAP tested



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                                                           Relationship between EuroNCAP Results and Real
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                                                                                          World Crashes


vehicles, more precise estimates of crashworthiness could be calculated. That is, the confidence limits
associated with the crashworthiness estimates calculated under the broad vehicle definitions were narrower
than those calculated using the original vehicle definitions.         The improvement in the precision of the
crashworthiness estimates is a result of the increased data available for analysis. Therefore, the differing
results reflect the different mix of vehicle model variants included in the vehicle definition and should not be
compared across the two sets of analyses. Rather the crashworthiness estimates should be interpreted as
the average risk of serious injury or death to drivers of the vehicle model variants included in the analysis.

Using the revised crashworthiness estimates derived from German mass crash data, a graphical analysis of
the correlation between crashworthiness estimates and overall EuroNCAP test scores, showed weak
evidence of a trend to improving EuroNCAP test scores with improvements in crashworthiness estimates.
The trend is very similar to that found in the original analysis of the German data, although, the reduced size
of the confidence limits further highlights the statistically significant variation in the estimated real crash injury
measures of vehicles within each overall EuroNCAP score range. That is, this re-analysis has provided
more conclusive evidence of the nature of the relationship between EuroNCAP test scores and
crashworthiness estimates.

The results of the graphical analysis were confirmed by analysis of the relationship between the two safety
measures using logistic regression. Focusing on crashworthiness as the primary measure of safety based
on mass crash data, 4 star rated vehicles were found to have an average crashworthiness significantly better
than lower star rated vehicles. However, 2 star rated vehicles had an estimated average crashworthiness
not statistically different to 3 star rated models.         These results point to improving average vehicle
crashworthiness with increasing EuroNCAP star ratings and confirm the results presented in SR-248 for the
German data. In addition, this analysis adds to the evidence from the other jurisdictions examined pointing
towards improving average vehicle crashworthiness with increasing EuroNCAP star ratings.

In summary, the results of the extended analysis of the German analysis mirror those presented in SR-248.
Testing the relationship between overall EuroNCAP test scores and crashworthiness on an individual vehicle
basis shows that whilst there is an association between the two measures, there is significant variation in
measures of injury outcome in real crashes for specific vehicles within each EuroNCAP score category. This
variation is a reflection of statistically significant differences between the measured real crash safety of
vehicles within the same star ratings. Therefore, a vehicle with a low crashworthiness estimate will not
always perform well in EuroNCAP testing and vice versa.


6.3       Alternative Weighting of NCAP Series to improve the Relationship
          to Real World Crashes (ST 2.4.)
Previous analysis of British, French and German police reported crash data show consistent trends to
improving crashworthiness with increasing EuroNCAP star ratings across the 2, 3 and 4 star rated vehicle
available for comparison. However, there remains significant variation in measures of injury outcome in real
crashes for specific vehicles within each EuroNCAP score category. This variation indicates that it may be



98
                                                         Relationship between EuroNCAP Results and Real
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                                                                                        World Crashes


possible to improve the two measures of vehicle safety. To achieve this, it is useful to consider in more
detail the relationship between estimated crashworthiness measures and EuroNCAP test results and, if
possible, improve the procedures for weighting EuroNCAP results to improve their relationship with real
world crash outcomes. To explore these issues analysis has been conducted using both British and German
real crash data and is reported here.

Method

The analysis completed in this study is divided into three components. The first, estimates vehicle safety
measures for all crash types based on real world crash data using combined data from Germany and Great
Britain.   The logistic regression procedure described in detail in SR-248 was used to estimate vehicle
crashworthiness and its constituent parts injury risk and injury severity. In order to obtain estimates of injury
risk and injury severity unbiased by factors other than vehicle make and model, a number of factors thought
to influence the risk and severity of injury to drivers were included in the logistic regression model.
Estimated vehicle safety measures for front and side impact crashes only are taken from the analysis of
British data conducted as part of sub-tasks 2.1 and 2.2. German data could not be used in analysis by crash
configuration as there is insufficient detail in the data to accurately determine the point of impact of a crash
involved vehicle.

The second analysis component examines the relationship between overall EuroNCAP test scores and
estimates of crashworthiness, injury risk and injury severity. Logistic regression techniques are used to
develop re-weighted EuroNCAP test scores for crashworthiness, injury risk and injury severity that better
correlate to measures of crashworthiness, injury risk and injury severity based on real world crash data. To
determine the relative weighting of individual components of the EuroNCAP test score that maximised
correlation between EuroNCAP test scores and estimated vehicle safety ratings a logistic framework is used.
In the case of the real crash crashworthiness measures, the logistic function fitted is of the following form.

log it (CWRi ) = α + β1 ( EuroNCAP front impact scorei ) + β 2 (EuroNCAP side impact scorei )

                                                                                                  …(Equation 1)

where i is the vehicle model index and α and β1 and β2 are parameters of the logistic model.

The third analysis component examines the relationship between the front and side impact components of
EuroNCAP test scores and the associated estimates of crashworthiness, injury risk and injury severity for
front and side impact crashes separately using the logistic regression techniques developed previously. In
the case of the real crash crashworthiness measures, the logistic function fitted is of the following form.

log it ( FCWRi ) = α + β1 (head/neck scorei ) + β 2 (chest scorei ) + β 3 (knee/femur/hip scorei )
+ β 4 (leg/foot scorei )

                                                                                             …(Equation 2)
where i is the vehicle model index and α, β1, β2, β3 and β4 are parameters of the logistic model.

Similarly, in the case of the real crash crashworthiness measure for side impact crashes, the logistic function
fitted is of the following form.



                                                                                                                 99
                                                                                            Relationship between EuroNCAP Results and Real
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                                                                                                                           World Crashes



log it ( SCWRi ) = α + β1 (head scorei ) + β 2 (chest scorei ) + β 3 (abdomen scorei ) + β 4 ( pelvis scorei )


                                                                                                                                   …(Equation 3)

where i is the vehicle model index and α, β1, β2, β3 and β4 are parameters of the logistic model.
A further set of analyses including vehicle mass as an additional explanatory variable is necessary to
account for the influence of vehicle mass on injury outcome.                                                      The effectiveness of re-weighting the
components of the relevant EuroNCAP scores is assessed by examining the R-squared value of a linear
regression between the re-weighted EuroNCAP measure and the relevant real world measure of injury
outcome.

Results- real crash based safety measures

A total of 69 vehicle models had sufficient real crash data to be included in the estimation of safety rating for
all crash types based on the combined British and German real world crash data. To confirm that measures
of vehicle safety estimated using combined data from Britain and Germany show the same general
relationship to EuroNCAP test results as previous analyses, the estimated crashworthiness ratings are
plotted against overall EuroNCAP scores.                                        Individual EuroNCAP scores are grouped according to the
corresponding star rating and 95 per cent confidence limits are place on the crashworthiness estimates.

                                      25.00%




                                      20.00%
       DfT Adjusted Crashworthiness




                                      15.00%




                                      10.00%




                                      5.00%




                                                          1 Star               2 Star                          3 Star               4 Star
                                      0.00%
                                               0            4         8         12              16              20        24         28        32

                                                                                        EuroNCAP Total Score



Figure 25                                          Overall EuroNCAP test score vs. estimated German real crash vehicle crashworthiness
based on all crash types.

Figure 26 shows significant variation in the injury measures of each vehicle estimated from Police reported
crash data within each overall EuroNCAP score range. This result is consistent with those achieved in
previous analyses of the relationship between the two safety measures.                                                     It is therefore reasonable to



100
                                                      Relationship between EuroNCAP Results and Real
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                                                                                     World Crashes


examine whether the procedures for comparing EuroNCAP results and real world crash outcomes can be
improved through logistic regression analysis.

Results- logistic regression analysis

The table below presents the R-squared values achieved by re-weighting the components of the relevant
EuroNCAP scores and regressing this value on the appropriate real world safety rating. The original R-
squared value presented in the table below results from a regression of the original EuroNCAP test score
against the relevant real world measure of injury outcome and represents a base level of correlation from
which to assess future improvements.

Table 23        Summary of R-squared measures for logistic regression analysis for all crashes and
by crash configuration
                                  All Crashes       Front Impact Crashes    Side Impact Crashes
R-Squared                   CWR      Injury  Injury CWR Injury      Injury CWR Injury      Injury
                                      Risk Severity         Risk Severity          Risk Severity
Original                    0.1088 0.0531 0.0932 0.0369 0.0082 0.0219 0.0857 0.0861 0.0378
Re-weighted (mass
adjusted)                   0.5235 0.7937    0.1564   0.4048 0.7519      0.1274   0.3443 0.7101     0.1316

Re-weighted (not mass
adjusted)                   0.082    0.05    0.0678   0.0238 0.0986      0.0792   0.2948 0.3425     0.1432

The results presented above demonstrate that by re-weighting the components of the relevant EuroNCAP
test scores, substantial improvements in the correlation of estimates of vehicle safety based on real world
crash data and EuroNCAP test scores can be achieved. This result holds primarily where adjustment is
made for vehicle mass. In general, the greatest improvement in correlation between the two measures of
vehicle safety is found for the injury risk measure. After adjusting for vehicle mass and re-weighting the
relevant components of the EuroNCAP score, in excess of 70 percent of variation in injury risk is explained
by the re-weighted EuroNCAP score for injury risk. This is likely due to the heavy influence of vehicle mass
on injury risk given involvement in a crash. In contrast, the smallest improvement in correlation between the
two measures of vehicle safety is found for the injury severity measure across the three crash types
considered. This is not unexpected given the lesser influence of vehicle mass on injury severity given that
some injury is sustained.

Discussion

Results of the analysis completed as part of sub-tasks 2.1 and 2.2 confirmed that the design priorities for
vehicle safety encouraged by the EuroNCAP scoring process are leading to improved real world crash
performance on average.      However, this study has been able to assess this potential for the overall
EuroNCAP test score as well as the front and side impact components of that score using logistic regression
techniques.

Considering the overall EuroNCAP test score, this study has demonstrated that, by re-weighting the front
and side impact components of that score, correlation between this measure and real world crashworthiness




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                                                        Relationship between EuroNCAP Results and Real
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                                                                                       World Crashes


can be improved substantially. The sign of the regression coefficient associated with the EuroNCAP front
and side impact test scores suggests that to optimise the relationship between the total score and real world
crashworthiness, it is necessary to negatively weight the front impact test score whilst positively weighting
the side impact test score. However, as there is significant correlation between the front and side impact
components of the overall EuroNCAP test score, any conclusions regarding the relationship between these
two components and injury outcome in real world crashes should be viewed as indicative only.

The relationship between the front impact component of the EuroNCAP test score and real world safety
ratings is contrary to that which might be expected, however, it is consistent with the findings in sub-tasks 2.1
and 2.2 that found no evidence of an association between average crashworthiness based on front impact
crashes and EuroNCAP offset frontal impact star ratings but a strong relationship between average
crashworthiness based on side impact crashes and EuroNCAP side impact star ratings. When combined
with the regression analysis conducted in this study, this highlights the need for further investigation of the
relationship between the front impact component of the EuroNCAP test score and real world injury outcomes
in front impact crashes.   Further investigation would likely require the use of in-depth real world crash data
containing detailed information on injuries sustained by the driver of the vehicle. The analysis could then
consider the correlation between the individual dummy measurements that comprise the EuroNCAP front
impact test and specific real world injury outcomes and would assist in determining whether the component
measures of the EuroNCAP front impact test are accurate predictors of real world injury outcome as
measured by crashworthiness, injury risk and injury severity. The feasibility of such a study is discussed in
more detail in SR-251.

The re-weighting process produces less conclusive results with respect to the relationships between
EuroNCAP front impact test components and real world front impact crashworthiness measures and
EuroNCAP side impact test components and real world side impact crashworthiness measures.
Nevertheless, there is evidence to suggest that substantial improvements to the correlation between the two
measures of injury outcome could be achieved by re-weighting the components of the EuroNCAP front and
side impact tests.

In addition to the individual results discussed above, the key outcome of the analysis relates to the role of
mass in the relationship between real world safety ratings and EuroNCAP test scores. Adjusting EuroNCAP
scores by mass has the most considerable impact on the relationship between the two measures of injury
outcome. This result highlights a crucial difference between the two measures of vehicle safety. The
EuroNCAP program is purported to be independent of vehicle mass and consumers are cautioned against
comparing the performance of vehicles from different size categories. In contrast, real world safety ratings
account for the influence of vehicle mass on injury outcome, as the crashes analysed are not restricted to
those occurring between vehicles of similar mass. It is clear from the analysis in this report, that adjustment
for vehicle mass in the EuroNCAP program would significantly improve the correlation between real world
safety measures and EuroNCAP test scores. However, the desirability of such an adjustment must be
considered in view of the aims of EuroNCAP and the potential of any change to unnecessarily increase the
appeal of heavier vehicles.


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Conclusion

Using logistic regression techniques this study has been able to assess the potential for improving the
relationship between EuroNCAP test scores and injury outcome in real world crashes as measured by
crashworthiness, injury risk and injury severity. By altering the relative weightings of the components of the
EuroNCAP test score and adjusting the score for vehicle mass, it has been shown that substantial
improvements in correlation between overall EuroNCAP test scores and real world injury outcomes can be
achieved. This has been tested for the relationship between the front and side impact components of the
overall EuroNCAP test score and real world safety ratings based on all crash types and the four components
of the front and side EuroNCAP test scores and estimates of real world safety estimated separately for front
and side impact crashes.

Some key conclusions and recommendations for future research arise from the analysis.

•   The process of re-weighting the EuroNCAP score was a data driven one and, therefore, this study
    provides no indication of the physical mechanisms driving the relationship between the two measures.

•   The research points to the need for more in-depth analysis of the relationship between individual
    components of the front impact test and real world injury outcomes. This would assist in determining
    whether the component measures of the EuroNCAP front impact test are accurate predictors of real
    world injury outcome as measured by crashworthiness, injury risk and injury severity.

•   Immediate improvement in the correlation between real world safety measures and EuroNCAP scores
    could be achieved by adjusting EuroNCAP test scores by a measure of vehicle mass.           However, any
    potential change to EuroNCAP must be considered in light of the aims of the program and the possible
    influence on vehicle purchasing patterns.

6.4      Possibilities of Relationship of NCAP Measures and Real World
         Injuries to Body Regions (ST 2.3)
SARAC 2 sub-tasks 2.1 and 2.1 have compared the relationship between injury outcomes in real crashes
and the results of EuroNCAP testing. In these comparisons, injury outcomes in real crashes have been
assessed at the broadest level with the real crash outcome measure being an average overall risk of death
or serious injury to the vehicle driver across all body regions. Similarly, only the aggregate EuroNCAP score
has been considered, either for the overall vehicle assessment of from each individual test configuration.

In this previous work, no attempt was made to compare the specific EuroNCAP test component outcome
scores by body region with injury outcomes to specific body regions of the driver in real-world crashes. This
was primarily because police crash data only indicate the overall injury severity to a driver using a coarse
four or five point scale of injury outcome. It does not generally code specific injuries and does not indicate
the specific regions of the body injured. Nor does it code injury severity to specific body regions or use an
overall severity scale with a finer gradient, such as the Abbreviated Injury Scale (AIS) or Injury Severity




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Score (ISS). This is because coding such detail in the data generally requires a level of information beyond
what it is possible for police to collect.

Being able to make comparisons between injury outcomes on a finer injury severity scale and by body region
with results from EuroNCAP testing by body region on the crash test dummy would allow much finer
assessment of the ability of EuroNCAP to reflect real world outcomes in crashes. It would allow assessment
of the ability of each EuroNCAP score component to reflect its corresponding specific real world injury
outcome. For example, it would allow assessment of the EuroNCAP offset frontal or side impact test head
assessment component in representing the risk of serious head injury in real world crashes of the same
configuration. Comparison on this specific basis would allow targeted evaluation of the relevance of each
EuroNCAP score component.

The aim of this sub task was to assess the suitability of available European in-depth crash data sources for
comparing real world crash outcomes by body region and on a finer injury severity scale with results of
EuroNCAP testing by body region.

Australian research experience

To illustrate the potential for assessment of EuroNCAP score components by body region, research
completed in Australia some years ago was reviewed (Newstead et al, 1997). The Australian research was
not able to utilise in-depth crash data since the Australian collection of such data was extremely limited.
Instead, the project was based on the analysis of data from third party insurance claims for injuries suffered
in motor vehicle crashes from the state of Victoria in Australia. The insurance data contained detailed coding
of injury outcomes to vehicle drivers that could be converted into AIS injury severity scores by body region of
injury and subsequently ISS scores for the driver. Analysis focused on comparing the average maximum AIS
scores by body region for injured drivers in each vehicle make and model with results of Australian NCAP
testing. It should be noted that the A-NCAP testing protocol and scoring system at the time of the study was
different to the current EuroNCAP practices with a full frontal and offset frontal impact test being carried out
and injury assessment based only on Head injury Criteria (HIC), Chest Acceleration (Chest G) and Femur
Loading (FL).

Although Victoria has over 15,000 claims for injury compensation from motor vehicle crashes each year, the
Victorian claims data contained information on only 2982 observations of driver injury across 28 ANCAP
tested vehicles. After excluding vehicle models with very small numbers of cases (less than 30), analysis
was limited to 19 vehicle models with full frontal A-NCAP scores and 12 vehicle models with offset A-NCAP
scores. For each vehicle model, the average maximum AIS score for each of the head, chest and leg regions
was calculated and compared to the corresponding A-NCAP score.

Tables 24 and 25 summarise the maximum AIS scores against the corresponding ANCAP readings (HIC,
Chest Loading and Femur Loading) for each of the three body regions for each vehicle model for full frontal
and offset ANCAP results respectively presented in the Australian study. Also included at the bottom of
these tables is the correlation between each ANCAP measure and the corresponding average maximum AIS
score as a measure of the association between the two variables.


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Table 24         Full Frontal ANCAP Test Results and Average Maximum AIS Scores by Body Region
for 19 ANCAP Tested Models
                 BODY REGION                         HEAD                CHEST            FEMUR
MODEL                                     No.     ANCAP         Av.   ANCAP       Av.   ANCAP         Av.
                                        Cases      HIC      Max.      Chest G Max. Femur L        Max.
                                                                AIS               AIS                 AIS
CIVIC (92-95)                             30      1456      0.00        63    0.13      3.20      0.23
NISSAN PATROL (88-90)                     32      1750      0.19        67    0.13      2.80      0.06
HYUNDAI EXCEL (95-96)                     36      1411      0.22        60    0.11      2.60      0.08
SUBARU LIBERTY (89-94)                    43      1360      0.09        58    0.16      3.90      0.21
MAZDA 626/MX6 (92-94)                     57      1160      0.14        60    0.35      2.60      0.26
TOYOTA CAMRY (93-96)                      65      1040      0.09        61    0.20      1.90      0.08
NISSAN PULSAR (92-95)                     66      1464      0.15        50    0.29      4.80      0.11
MAZDA 121 (91-96)                         72      1525      0.11        61    0.21      4.70      0.22
FORD FALCON EF (94-96)                    104      910      0.27        74    0.18      7.40      0.23
NISSAN PINTARA (89-92)                    107     1750      0.11        64    0.26      2.40      0.20
FORD LASER (91-94)                        129     1903      0.20        68    0.25      8.60      0.26
HYDAI EXCEL (90-94)                       139     1318      0.20        54    0.14      3.60      0.19
BARINA (89-93)                            146     1005      0.14        59    0.25      3.90      0.16
FORD FALCON EB SERIES II (92-94)          156     1340      0.11        74    0.16      6.00      0.15
MITSUBITSHI MAGNA TR/TS (91-95)           163     1140      0.08        60    0.15      3.80      0.15
HOLDEN COMMODORE VR/VS (93-96)            194     1170      0.08        51    0.15      3.20      0.15
TOYOTA COROLLA (90-94)                    294     1499      0.13        60    0.21      9.40      0.19
TOYOTA CAMRY (88-92)                      353     1090      0.08        63    0.28      3.90      0.15
HOLDEN COMMODORE VN/VP (87-93)            640     1690      0.19        82    0.21      1.20      0.16
                 CORRELATION                      HIC with Av.        CG with Av.        FL with Av.
                  ANALYSES                         Max. AIS to         Max. AIS to       Max. AIS to
                                                     HEAD                CHEST             LEGS
                                     All Models          0.12             -0.06                0.40
                                                   (p=0.3148)          (p=0.5949)        (p=0.0451)



Table 24 shows a strong statistically significant association between full frontal ANCAP femur loading
readings and average maximum AIS to the leg region in real crashes for the 19 models included in the
analysis. Whilst table 1 also shows indication of a weak association between HIC and real crash head injury
severity for this ANCAP test configuration, the result is not statistically significant. No association between
full frontal ANCAP chest loading and maximum AIS to the chest region in real crashes was observed. Table
25 shows a strong statistically significant association between the offset ANCAP chest loading and average




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maximum AIS to the chest in real crashes for the 12 car models for which offset scores are available. No
association was found between ANCAP and real crash measures for the head or leg regions.

Table 25         Offset ANCAP Test Results and Average Maximum AIS Scores by Body Region for 12
ANCAP Tested Models
                 BODY REGION                           HEAD           CHEST               FEMUR
MODEL                                    No.        ANCAP    Av.   ANCAP        Av.   ANCAP Av. Max.
                                        Cases        HIC    Max. Chest G    Max.      Femur L      AIS
                                                             AIS                AIS
CIVIC (92-95)                                  30    623     .03    40          .13    1.30        .23
NISSAN PATROL (88-90)                          32    897     .00    37          .13    4.60        .06
HYUNDAI EXCEL (95-96)                          36 1270       .06    49          .11    4.70        .08
TOYOTA CAMRY (93-96)                           65    640     .03    42          .20    3.50        .08
NISSAN PULSAR (92-95)                          66 2161       .03    78          .29    18.00       .11
MAZDA 121 (91-96)                              72 1566       .01    69          .21    7.40        .22
FORD FALCON EF (94-96)                     104       596     .11    53          .18    3.70        .23
FORD LASER (91-94)                         129 3234          .07    84          .25    11.20       .26
HYDAI EXCEL (90-94)                        139 1195          .06    58          .14    4.90        .19
BARINA (89-93)                             146 1213          .03    56          .25    8.30        .16
HOLDEN COMMODORE VR/VS (93-96)             194       730     .09    37          .15    2.60        .15
TOYOTA COROLLA (90-94)                     294 1024          .06    52          .21    6.20        .19
                 CORRELATION                        HIC with Av.    CG with Av.         FL with Av.
                  ANALYSES                          Max. AIS to     Max. AIS to         Max. AIS to
                                                       HEAD           CHEST               LEGS
            Models with offset ANCAP scores            -0.04             0.74              -0.01
                                                    (p=0.5478)      (p=0.0022)          (p=0.5120)



The Australian analysis shows the potential for comparing injury severity outcomes in real crashes and
NCAP testing score components by body region and using the finer AIS injury severity scoring system. It
was able to identify significant correlations between score components and real crash outcomes in specific
body regions in each of the test configurations considered. The analysis was limited, however, in that it did
not include adjustment for differences in driver characteristics and crash circumstances between vehicle
models that may have confounded the results presented. Ideally, analysis should have included adjustment
of such differences. However, the limited data available for analysis meant the adjusted analysis was not
feasible.




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European in-depth data sources

Two European in-depth crash inspection data sources were made available for assessing the general
suitability of such data sources for examining the relationship between real crash outcomes and EuroNCAP
test results by body region of injury.

CCIS

The UK Co-operative Crash Injury Study (CCIS) is funded by the UK Department for Transport (DfT), Ford,
Toyota, Renault PSA and Autoliv with data being collected by specialist teams a Loughborough and
Birmingham Universities and an agency of the DfT. The teams investigate crashes and compile vehicle
examination evidence and injury details on about 1500 cases per year, the majority coming from the East
and West Midlands region of England. The data represents a stratified sample of crashes by injury severity
involving passenger cars less than 8 years old and towed from the scene. The data can be weighted to be
representative of the population although the unweighted data is intentionally biased towards the most
serious injuries occurring in newer cars with damage. The data sampling and collection protocol has been
relatively unchanged since 1983 with the total database containing well over 10,000 records.

A sample of CCIS data was provided for the study. It comprised records on 3098 vehicles from model series
that had been tested by EuroNCAP at some point. Data were only provided in tabular and not unit record
format and covered overall counts of vehicles as well as maximum AIS by body region of injury.

PENDANT

Pendant is the acronym for the Pan-European Co-Ordinated Accident and Injury Databases, an EC funded
project co-ordinated by Loughborough University in England and with partners from organisations across
Europe. The PENDANT project aims to assemble a number of different databases of different size and
severity from various European sources including hospital data and in-depth crash inspection data.
Consideration in this report focuses on the in-dept data being assembled as part of PENDANT Work
Package 2.

Work Package 2 of PENDANT brings together the resources and infrastructures of existing accident and
injury investigation groups to build a demonstration European Crash Injury database. The project aimed to
have 1100 cases in the demonstration database although it is anticipated the project would facilitate ongoing
harmonized data collection. The principal aim of analysis from the database was to examine the injury
prevention priorities for future action and to provide feedback to casualty reduction measures such as the
EuroNCAP rating system. Content of the database is significantly more detailed than that found in police
reported crash data.

Groups collecting the data cover northern, middle and southern Europe giving a representative range of
accident conditions. A special feature of the data is the case selection methodology that targets coverage of
newer vehicles to give data that has most value for regulation and safety countermeasures. Only crashes
involving a vehicle registered on or after the 1st January 1998 are collected with the age of the partner
vehicle in a multi vehicle crash not important. Furthermore, crashes are only included in the sample if they




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involve an injured car occupant, but not necessarily to the occupant of the 1998 or later vehicle. Single
vehicle crashes are included if the car is registered from 1998 onwards and an occupant is injured. At least
20% of the cases in the PENDANT in-depth data are severe crashes involving a fatality or AIS 3+ injury.
Remaining crashes are selected at random although must still meet the previous criteria. Representative
weighting is provided for each crash.

A sample of the PENDANT in-depth data was provided for assessment in the study on a unit record basis
and covered 1322 crash involved vehicles. A wide range of relevant fields from the database was included.

Suitability of in-depth data sources

Suitability of both the CCIS and PENDANT databases for assessing the relationship between EuroNCAP
tests and real crash outcomes by body region and more specific injury severity outcome are related to the
content and coverage of the data.

CCIS data

Assessment of the content of the CCIS data was problematic as the data was only supplied in summary
tabular format and not on a case by case basis. There appeared to be sufficient data on about 20 different
NCAP tested vehicle models that had more than 30 examples of crashes vehicles in the database for
meaningful analysis on an aggregate basis. Make and model of vehicle by year of manufacture were given to
allow specific matching of EuroNCAP tested vehicles. AIS injury outcomes were also available by body
region although when disaggregated to this level the number of cases became relatively sparse meaning
analysis results would likely be of limited accuracy. Despite this, it appeared possible to analyse the CCIS
data on this basis.

It is known that the CCIS data in unit record format contain information on driver characteristics such as age
and gender and details of the crash circumstances although tabulation of these variables was not provided in
the sample data. Differences in these variables between vehicle models could be controlled for in an
analysis of the CCIS data on a unit record basis. Not having access to the CCIS data on a unit record basis
represented a key limitation of the data for the purpose being assessed.

Another concern of the CCIS data for the EuroNCAP comparison by body region was the absence of a
representative sample of non-injury data in the sample meaning injury risk could not be assessed. This is not
considered a fatal flaw since other studies have indicated that EuroNCAP is more representative of relative
injury severity rather than injury risk. However, the CCIS database is known to be biased towards most
serious injuries so, unless suitable weighting factors could be provided for each case, it may be difficult to
use the data effectively for assessing relative injury severity. In addition, since CCIS is focused towards
vehicle occupants, use of the database to assess relationships with the EuroNCAP pedestrian test would not
be possible.

PENDANT data

A fuller assessment of the PENDANT in-depth data was possible since essentially the complete database
was supplied for assessment on a unit record basis. The content of the data was highly detailed in its


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coverage of information. Key fields required for the EuroNCAP comparison by body region were all present
including AIS injury outcome by body region for each case. Make and model of vehicle and year of
manufacture were also given in the data to allow accurate matching with EuroNCAP tested vehicle models.

For the same reasons as CCIS, PENDANT data could not be used to assess injury risk. However, use of the
PENDANT data to assess relative injury severity would be feasible considering representative weighting
factors for cases in the database are available. The data also covers pedestrian injury outcome meaning
comparisons with the EuroNCAP pedestrian test outcomes would be possible. Information about crash
circumstances and occupant characteristics are also available to allow a controlled analysis to be
undertaken.

Reflecting the fact that PENDANT is an example harmonised European in-depth database, its key problem
for use in comparing with EuroNCAP outcomes is the coverage of the database. Despite crash data on over
1300 vehicle being available, when analysed by vehicle make and model, only 1 vehicle model had more
than 30 cases in the database before restriction to specific years of manufacture for matching with the
EuroNCAP test outcomes. Clearly this is insufficient to allow analysis of injury outcome at an overall level let
alone by specific body region and injury severity level. Clearly ongoing collection of the PENDANT in-depth
data would have to happen before it was ultimately suitable for the analysis purpose being assessed.

Summary

Both the CCIS and PENDANT databases appear in theory to be suitable for comparing to EuroNCAP results
by body region and more specific levels of injury severity. Both have the information on injury severity by
body region and sufficient details on vehicle model and year of manufacture to match with EuroNCAP test
data. They also both appear to contain the required range of variables to undertake analysis controlled by
driver and crash characteristics between vehicle models. This was less certain for the CCIS data from the
sample supplied.

Coverage of both datasets was inadequate to measure injury risk but both could be used to measure relative
injury severity that would suit the purpose of the planned comparisons. The PENDANT data was most
suitable in this respect as it included representative case weights whereas CCIS did not appear to contain
weights.

Both databases were of insufficient size with the number of cases of EuroNCAP tested vehicle models in the
data not great enough to undertake meaningful analysis, particularly when disaggregated by body region of
injury. Furthermore, the CCIS data would need to be provided in unit record format to be entirely useful for
analysis.

Summary of data requirements for future analysis

The limitations of both the sample CCIS and PENDANT in-depth crash data meant meaningful analysis of
the relationship between EuroNCAP test outcome and real world injury levels by body region could no be
meaningfully undertaken. The key output of the sub-task review then was a summary of ideal requirements
of in-depth data. These are as follows.




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      •   Data should be provided in unit record format for analysis where every case in the database
          represents a driver or front left passenger involved in a crash in a vehicle model tested by
          EuroNCAP or a pedestrian struck by a EuroNCAP tested vehicle model. Analysis of individual case
          data allows maximum opportunity to control for factors external to the vehicle in determining injury
          outcome hence making the comparison with EuroNCAP results more pure. The external factors may
          relate to either the injured occupants physical characteristics, their mode within the vehicle, the crash
          impact severity and crash configuration.

      •   Vehicle make an model information and year of manufacture should be supplied directly or be able
          to be derived from, for example, Vehicle Identification Number, to allow precise selection of vehicle
          models for comparison with the corresponding EuroNCAP test. Alternatively, each record should
          have a code linking it to the appropriate EuroNCAP test information if vehicle model details are not to
          be provided explicitly.

      •   Factors independent from the vehicle that ideally should be recorded in the individual case data for
          analysis, in rough order of priority by group are as follows:

              Occupant Characteristics

                  •    Injury outcome by body region (AIS, ICD)

                  •    Seating position

                  •    Age

                  •    Gender

                  •    Restraint use

                  •   Height

              Crash Characteristics

      •   Impact severity (Delta V or EBS)

      •   Impact point on the vehicle

      •   Impact angle

      •   Impact partner vehicle details including body type, mass, point of impact (multi-vehicle crashes)

      •   Object struck (single vehicle crashes or where relevant)

              Pedestrian Characteristics

                  •    Age

                  •    Gender

                  •    Height



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    •   The data sampling frame should be well articulated, particularly with respect to injury and crash
        severity levels. Ideally weightings to make the data representative of the broader crash population
        should be provided to allow comparative injury severity outcomes between vehicles to be accurately
        represented.

    •   The data should contain a reasonable number of cases on each EuroNCAP tested vehicle model to
        allow meaningful analysis to be undertaken. Although the exact number required is difficult to
        quantify without estimates of the effect sizes to be measured, a rough guide of a minimum of 30
        cases per vehicle would be reasonable although significantly more cases could be required for more
        complex comparisons, including those adjusted for confounding factors.

Of the two sample in-depth data sets reviewed, the PENDANT data comes closest to meeting the above
requirements apart from sample size. If data collection under the PENDANT framework is continued it could
be useful for the proposed analysis by body region and specific injury severity outcome in the future.

Other Possible Data Sources

As demonstrated by the Australian analysis correlating injury outcome by body region with A-NCAP results,
other sources of data exist to facilitate such comparisons. The Australian analysis demonstrates the
successful use of an injury compensation claims database for the analysis. Other data that may be useful
are hospital admissions databases such as that being assembled in Europe under Working Package 3 of the
PENDANT project. To be useful for the purpose of the proposed analysis, the data would have to conform to
the ideal requirements of the in-depth data listed above. In general, this would require the claims or hospital
data to be linked to suitably detailed crash records and potentially vehicle registration records to provide the
required crash and vehicle information. The key benefit of such data is that it provides many more cases for
analysis than can typically be obtained from in-depth crash inspection databases but still has rich information
in occupant and pedestrian injury outcomes that can be used to derive the required injury outcomes by body
region. The availability and potential use of insurance claims and hospital admission databases in Europe
should be investigated as part of future research.

6.5      Analysis of Car/Pedestrian Crash Data from Great Britain,
         Germany and France (ST 3.4.)
SARAC II sub-tasks 2.1 and 2.2 produced individual estimates of vehicle crashworthiness for all crash types,
front impact crashes and side impact crashes.        The analysis reported here explores the feasibility of
assessing pedestrian injury outcomes as a function of the colliding vehicle model using real crash data
sources from Great Britain, France and Germany. The second component of this study is a descriptive
analysis of pedestrian crash data from Great Britain, France and Germany. The analysis examines the
distribution of crashes across vehicle models by road and demographic characteristics that influence
pedestrian injury outcome. Additional factors that were not found to influence injury outcome in the logistic
regression analysis are also presented.




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Data Sources

Police reported pedestrian crash data from Great Britain for the period 1993 to 2001, from France for the
period 1999 to 2001 and from Germany for the period 1998 to 2002 is used as the basis of this analysis.
Adopting the criteria of at least 80 pedestrian injuries for each EuroNCAP tested vehicle model 42, 16 and 24
vehicle models from the British, French and German data respectively had sufficient data to be included in
the analysis.

Method

Just as vehicle crashworthiness ratings were calculated for occupants of EuroNCAP vehicles involved in real
crashes in sub-tasks 2.1 and 2.2, the same methods can be used to rate the injury likelihood to pedestrians
as a function of the colliding vehicle model. The measure of pedestrian injury outcome used here is that
developed by Cameron et al (1998) that they termed a measure of vehicle aggressivity towards unprotected
road users.     However, in this study the measure is applied to pedestrians only.       As all or nearly all
unprotected road users involved in Police reported crashes are injured to some degree Cameron et al (1998)
defined their measure of aggressivity towards unprotected road users as the risk of death or serious injury
given some level of injury is sustained. In the same way as for vehicle injury severity, pedestrian injury
severity is estimated via logistic regression techniques where the pedestrian injury status (death or serious
injury/other injury) is the dependent variable and is modelled as a function of the colliding vehicle make and
model. In order to obtain estimates of pedestrian injury severity unbiased by factors other than vehicle make
and model, a number of crash and pedestrian characteristics thought to influence pedestrian injury severity
were included in the logistic regression model.

In assessing the relationship between the real crash pedestrian safety ratings and the EuroNCAP pedestrian
star ratings the method adopted in sub-tasks 2.1 and 2.2 for all crash types, front impact crashes and side
impact crashes has been used. This method is described in detail in Section 4.4 of the SR-248. The
pedestrian test protocol is described in detail in EuroNCAP (2002) and the relevant EuroNCAP tested star
ratings were taken from the EuroNCAP website (www.euroncap.com).         All vehicle models included in the
analysis were tested prior to 31 December 2001 so the star ratings obtained for each vehicle model are
comparable.     However, it is noted that changes in the testing procedure were experienced at Phase 3.
From this time, changes to the assessment criteria occurred and manufacturers were also allowed to select
some of the vehicle test sites themselves. In addition, the analysis reported here considers one and two star
pedestrian rated vehicles only as no three star pedestrian rated vehicles were available for analysis at the
commencement of this study.

Vechicle injury severity ratings for pedestrian crashes

Tables 26 to 28 below present the estimates of relative pedestrian injury severity for Great Britain, France
and Germany respectively. The severity estimates are interpreted as the adjusted probability of pedestrian
death or serious injury given some injury was sustained in a police reported crash.




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Table 26        Injury severity estimates for EuroNCAP tested vehicles for pedestrian crashes only:
Great Britain
      Index           Make                Model              Severity   Lower    Upper     Width of
                                                                        95% CI   95% CI      CI
           1       Fiat        Punto 55S                      25.42      22.52    28.55     6.03
           2       Ford        Fiesta 1.25 LX 16V             24.04      22.07    26.14     4.07
           3       Nissan      Micra 1.0L                      24.5      21.06     28.3     7.24
           5       Rover       100                            25.03      21.77     28.6     6.83
           6       Vauxhall    Corsa 1.2LS                     31.3      23.03    40.96     17.93
           7       Volkswag    Polo 1.4L                      21.16      18.13    24.54     6.41
                   en
        8          Audi        A4 1.8                         26.14     19.16     34.59      15.43
        9          BMW         316i                           23.93      20.8     27.37       6.58
        10         Citroen     Xantia 1.8i Dimension          26.25     22.39     30.52       8.13
        11         Ford        Mondeo 1.8LX                   26.58     23.89     29.47       5.58
        12         Mercedes    C180 Classic                   28.81     21.59     37.29      15.71
        13         Nissan      Primera 1.6GX                  16.48     11.89     22.39       10.5
        14         Peugeot     406 1.8LX                      21.06     17.69     24.87       7.17
        15         Renault     Laguna 2.0RT                   21.48     17.25     26.41       9.16
        16         Rover       620 Si                         22.92     18.78     27.66       8.88
        18         Vauxhall    Vectra 1.8iLS                  25.83     23.28     28.54       5.26
        21         Citroen     Xsara 1.4i (LHD)               20.31     13.03     30.26      17.24
        22         Daewoo      Lanos 1.4SE (LHD)              21.63     13.95     31.96      18.01
        23         Fiat        Brava 1.4S                     26.04     22.18      30.3       8.11
        24         Honda       Civic 1.4i                     23.33     20.09     26.91       6.82
        25         Hyundai     Accent 1.3GLS (LHD)            26.16     18.52     35.58      17.06
        27         Peugeot     306 1.6GLX                      25.9     22.54     29.57       7.04
        28         Renault     Megane 1.6RT (LHD)             23.84     19.92     28.26       8.34
        30         Toyota      Corolla 1.3 Sportif (LHD)      22.42     16.63     29.51      12.88
        37         Vauxhall    Omega 2.0Gl/GLS (LHD)          22.54     18.17     27.62       9.45
        39         Ford        Focus 1.6 (LHD)                25.99     22.04     30.38       8.34
        41         Vauxhall    Astra 1.6i Envoy                26.5     23.45      29.8       6.35
        42         Ford        Escort 1.6 LX                  24.65     22.96     26.44       3.48
        43         Nissan      Almera 1.4GX                   30.98      23.5     39.61      16.11
        56         Vauxhall    Corsa 1.0 12v Club             24.96     21.11     29.26       8.15
        63         Ford        Ka 1.3 (LHD)                   26.53     22.35     31.18       8.83
        64         Volvo       S40 1.8                        24.73     17.54     33.66      16.12
        65         Toyota      Avensis 1.6S                   22.44      16.6     29.61      13.02
        66         Citroen     Saxo 1.1 SX (LHD)              28.24     24.98     31.74       6.76
        70         Ford        Fiesta 1.25 Zetec              25.78     20.59     31.75      11.17
        72         Peugeot     206 1.3 XR Presence (LHD)      28.07     22.06     34.97      12.91
        73         Renault     Clio 1.2 RTE (LHD)             21.68     16.27     28.28      12.01
        84         BMW         316i (LHD)                     23.64     16.88     32.05      15.17
        89         Peugeot     406 (LHD)                      21.16     14.85     29.24      14.39
        93         Vauxhall/   Vectra 1.8 (LHD)               21.39     16.68       27       10.33
                   Opel
        94         Volkswag    Passat 1.9 Tdi (LHD)            24.6      17.5     33.43      15.93
                   on
       102         Renault     Scenic 1.4 (LHD)               25.38     17.67     35.03      17.35




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Table 27       Injury severity estimates for EuroNCAP tested vehicles for pedestrian crashes only:
France
      Index          Make               Model               Severity   Lower    Upper     Width of
                                                                       95% CI   95% CI      CI
           1      Fiat       Punto 55S                      24.07%     18.79%   30.28%    11.49%
           2      Ford       Fiesta 1.25 LX 16V             21.97%     16.59%   28.50%    11.91%
           7      Volkswag   Polo 1.4L                      23.69%     16.46%   32.85%    16.40%
                  en
         10       Citroen    Xantia 1.8i Dimension          26.08%     19.83%   33.49%     13.66%
         14       Peugeot    406 1.8LX                      21.38%     15.54%   28.67%     13.13%
         15       Renault    Laguna 2.0RT                   22.86%     15.78%   31.91%     16.13%
         21       Citroen    Xsara 1.4i (LHD)               25.12%     17.61%   34.49%     16.88%
         27       Peugeot    306 1.6GLX                     30.71%     25.48%   36.49%     11.01%
         28       Renault    Megane 1.6RT (LHD)             29.24%     20.53%   39.80%     19.27%
         41       Vauxhall   Astra 1.6i Envoy               23.98%     15.90%   34.48%     18.58%
         42       Ford       Escort 1.6 LX                  22.64%     16.49%   30.24%     13.74%
         44       Renault    Espace 2.0RTE (LHD)            25.57%     17.11%   36.38%     19.27%
         56       Vauxhall   Corsa 1.0 12v Club             17.53%     10.78%   27.22%     16.44%
         66       Citroen    Saxo 1.1 SX (LHD)              26.95%     21.38%   33.36%     11.98%
         73       Renault    Clio 1.2 RTE (LHD)             22.81%     17.24%   29.53%     12.29%
         89       Peugeot    406 (LHD)                      24.86%     17.63%   33.83%     16.20%



Table 28       Injury severity estimates for EuroNCAP tested vehicles for pedestrian crashes only:
Germany
      Index          Make               Model               Severity   Lower    Upper     Width of
                                                                       95% CI   95% CI       CI
           1      Fiat       Punto 55S                      39.84%     33.90%   46.10%    12.20%
           3      Nissan     Micra 1.0L                     37.46%     31.00%   44.39%    13.40%
           4      Renault    Clio 1.2RL                     42.78%     32.41%   53.83%    21.42%
           6      Vauxhall   Corsa 1.2LS                    38.95%     34.46%   43.64%     9.18%
           7      Volkswag   Polo 1.4L                      42.92%     36.66%   49.42%    12.76%
                  en
         8        Audi       A4 1.8                         34.24%     27.71%   41.44%     13.73%
         9        BMW        316i                           39.43%     29.57%   50.23%     20.66%
         11       Ford       Mondeo 1.8LX                   39.94%     30.32%   50.41%     20.08%
         12       Mercedes   C180 Classic                   35.75%     31.30%   40.46%     9.16%
         18       Vauxhall   Vectra 1.8iLS                  44.34%     37.10%   51.82%     14.71%
         19       Volkswag   Passat 1.6L (LHD)              35.33%     27.05%   44.60%     17.54%
                  en
         20       Audi       A3 1.6                         37.70%     29.06%   47.20%     18.13%
         24       Honda      Civic 1.4i                     42.11%     34.14%   50.51%     16.38%
         28       Hyundai    Accent 1.3GLS (LHD)            44.35%     35.43%   53.64%     18.21%
         31       Volkswag   Golf 1.4 (LHD)                 39.06%     32.95%   45.55%     12.60%
                  en
         33       BMW        520i (LHD)                     43.00%     33.50%   53.04%     19.53%
         40       Mercedes   A140 Classic (LHD)             30.18%     21.18%   41.01%     19.83%
         41       Vauxhall   Astra 1.6i Envoy               32.49%     27.68%   37.71%     10.03%
         42       Ford       Escort 1.6 LX                  46.63%     40.99%   52.37%     11.38%
         43       Nissan     Almera 1.4GX                   41.85%     32.22%   52.14%     19.91%


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                                            53           Volkswag   Lupo 1.0 (LHD)                        41.51%      31.54%   52.24%   20.70%
                                                         en
                                            54           MCC        Smart (LHD)                           40.63%      32.17%   49.68%   17.52%
                                            56           Vauxhall   Corsa 1.0 12v Club                    42.30%      36.49%   48.34%   11.85%
                                            63           Ford       Ka 1.3 (LHD)                          39.48%      33.09%   46.25%   13.15%



Although there was sufficient real world crash data to enable the estimation of pedestrian injury severity
estimates, the width of the associated confidence intervals was large making it difficult to differentiate the
pedestrian safety performance of individual vehicle models. This is primarily of function of the relatively
small amounts of pedestrian crash data available for analysis.

The relationship between pedestrian injury outcomes in real crashes and euroncap pedestrian
protection ratings

The analysis that follows considers the relationship between pedestrian safety ratings based on real world
crash data and thee EuroNCAP pedestrian star ratings available from the EuroNCAP website. The results of
the graphical analysis are presented below in Figures 27 to 29.

                                            45.00%



                                            40.00%



                                            35.00%
      Adjusted Pedestrian Injury Severity




                                            30.00%



                                            25.00%



                                            20.00%



                                            15.00%



                                            10.00%



                                             5.00%



                                             0.00%
                                                             1                                                  2


                                                                                    EuroNCAP Pedestrian Star Rating




Figure 26                                            Pedestrian EuroNCAP star rating vs. Adjusted injury severity: Great Britain




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                                            45.00%



                                            40.00%



                                            35.00%
      Adjusted Pedestrian Injury Severity




                                            30.00%



                                            25.00%



                                            20.00%



                                            15.00%



                                            10.00%



                                             5.00%



                                             0.00%
                                                                       1                                                  2

                                                                                    EuroNCAP Pedestrian Star Rating



Figure 27                                            Pedestrian EuroNCAP star rating vs. Adjusted injury severity: France




                                            60.00%




                                            50.00%
      Adjusted Pedestrian Injury Severity




                                            40.00%




                                            30.00%




                                            20.00%




                                            10.00%




                                            0.00%
                                                                   1                                                  2


                                                                                  EuroNCAP Pedestrian Star Rating



Figure 28                                            Pedestrian EuroNCAP star rating vs. Adjusted injury severity: Germany




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Each of the figures shows significant variation in the point estimates of pedestrian injury severity within each
overall EuroNCAP star rating. Nevertheless, the overlapping confidence limits for one star and two star
rated vehicles indicates that there is no evidence of a relationship between EuroNCAP pedestrian star
ratings and the pedestrian injury severity estimates generated from real world crash data from any of the
three countries. These results are confirmed by a formal logistic regression analysis of the relationship
between the two measures of injury outcome.

There are a number of potential factors that may contribute to the lack of significant evidence of a trend to
improving real world pedestrian injury outcome with increasing EuroNCAP pedestrian star ratings in the
British, French and German data.       It is possible that there is in fact no relationship between the two
measures of pedestrian protection. At Phase 3 of EuroNCAP testing, vehicle manufacturers were allowed
select some of the vehicle test sites themselves. This may have resulted in changes in test scores without
any corresponding change in the level protection offered to pedestrians by the vehicle. A full description of
changes to the EuroNCAP pedestrian test protocol and their effect on test scores can be found in Ponte et
al. (2004).

A second possible reason for the lack of relationship between EuroNCAP star ratings and real world
pedestrian safety ratings is the method of test site selection under EuroNCAP. The selection of test sites is
not directly related to the relative frequency of real world crashes involving those sites. Therefore, the
selection of a poor performing test site will be reflected in the EuroNCAP pedestrian test score and star
ratings regardless of the relative frequency of a pedestrian hitting that site. Similarly, the selection of a well
performing test site will be reflected in the EuroNCAP pedestrian test score and star ratings regardless of the
relative frequency of a pedestrian hitting that site. In contrast, real world pedestrian safety ratings implicitly
account for the relative frequency of point of pedestrian impact. Given the difference in the relationship
between pedestrian point of impact and measured outcome for EuroNCAP and real world safety ratings, it is
perhaps unsurprising that no correlation between the two measures of pedestrian safety has been found.

Finally it is noted that the analysis reported here considers one and two star rating vehicles only as no three
star rated vehicles were available for analysis at the commencement of this study.

Descriptive analysis

The final component of this study provides a descriptive analysis of the pedestrian data used to generate the
estimates of pedestrian injury severity presented above. The analysis examines the distribution of non-
vehicle factors across the EuroNCAP tested vehicles used in the preceding analysis.

Plotting the distribution of pedestrian injury severity across vehicle models using data from France, German
and Great Britain reveals variation in unadjusted pedestrian injury severity by vehicle model across the three
countries. Some of this variation may be explained in the following analysis of factors likely to influence
pedestrian injury severity.

Crash characteristics




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Crash impact severity is likely a significant influence on pedestrian injury outcome. However, as this variable
is not directly available in any of the data sources alternative measures such as the speed zone or
urbanisation of the crash location and an intersection indicator are used as proxies for impact severity.
Analysis of the British data reveals that a higher proportion of fatalities and serious injuries occur in speed
zones of 60 mph or above than in speed zones of less than 60 mph. In contrast, there is little apparent
difference between the proportion of pedestrians killed or seriously injured in crashes with passenger
vehicles at intersections compared with crashes not occurring at intersections.

Given these variations, it is interesting to also consider the distribution of these proxy measures by vehicle
model. Significant variation in these measures by vehicle model would suggest that it is necessary to adjust
for these factors in estimating pedestrian injury severity estimates such as those presented previously. The
analysis of the French, German and British data shows that there is variation in the urbanisation and speed
limit of the crash site by vehicle model. Similarly, there is variation in the location of the crash site by vehicle
model. The analysis by speed zone of the British pedestrian crash data provides additional information
about the average distribution of pedestrian crashes by speed zone. It is evident that across all vehicle
models, the vast majority of pedestrian crashes occur in 30mph speed zones. Across the other speed limit
zones there is some variation by vehicle model.

When viewed in combination these results support the need to adjust for impact severity proxy factors when
estimating pedestrian injury severity estimates. This is particularly so for the speed limit (or urbanisation) of
the crash site where there is evidence of an influence of crash location on pedestrian injury outcome.




Pedestrian characteristics

In addition to crash site variables, the characteristics of the pedestrian may influence pedestrian injury
outcome in a collision with a vehicle. Investigation of the influence of pedestrian characteristics on injury
outcome reveals illustrates that the proportion of pedestrians killed or seriously injured in collisions with a
passenger vehicle differs by the age of the pedestrian. It is evident that a higher proportion of elderly
pedestrians suffer fatal or serious injuries compared to pedestrians aged under 65. In contrast, there is little
apparent difference between the proportion of male and female pedestrians killed or seriously injured in
crashes with passenger vehicles.

Given these variations, variation in pedestrian age and pedestrian sex by vehicle model are examined for
France, Germany and Great Britain respectively. Significant variation in these measures by vehicle model
would suggest that in estimating pedestrian injury severity estimates such as those presented previously, it is
necessary to adjust for these factors. Analysis reveals that there is some variation in the proportion of male
and female involvement in pedestrian crashes by vehicle model particularly in the French and German data.
There is also variation in the proportion of male and female involvement in pedestrian crashes by vehicle




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model in the British data. However, for all vehicle models examined, the majority of the pedestrians hit in
Police reported crashes are male.

The analysis of pedestrian age indicates that in France for each vehicle model the majority of pedestrians
involved in Police reported crashes are aged between 0 and 15 years or aged over 60 years. In Germany,
the highest proportion of crash involved pedestrians for each vehicle model are aged under 25 years. In
Great Britain, those aged between 0 and 15 represent the highest proportion

Conclusion

Using the pedestrian data supplied to MUARC as part of the SARAC II study, it has been possible to
investigate the nature of pedestrian crashes in Great Britain, Germany and France. Three key conclusions
arise from this analysis.

First, sufficient data from these countries was made available to enable the estimation of pedestrian injury
severity in collisions with passenger vehicles by vehicle model for each country adjusted for effects of non-
vehicle factors on injury outcome. These estimates represent the risk of death or serious injury to a crash
involved pedestrian given that some injury was sustained in a Police reported crash. However, although
there was sufficient data to enable the estimation of these measures, the width of the associated confidence
intervals was large. Therefore, it difficult to differentiate the pedestrian safety performance of individual
vehicle models. The width of the confidence limits is primarily a function of the still relatively small amounts
of pedestrian crash data available for analysis. More data would be required to increase the precision of the
estimates.

Second, no clear evidence of a relationship between EuroNCAP pedestrian star rating and the pedestrian
injury severity estimates generated from real world crash data from Great Britain, Germany and France was
found. This indicates that the EuroNCAP pedestrian star rating is not significantly associated with pedestrian
injury severity estimates based on real crash data. Further, given past estimates of the relationship between
these two measures of pedestrian injury outcome, it is unlikely that the inclusion of additional data would
alter this result.

Finally, the descriptive analysis reported highlights the influence of non-vehicle factors by pedestrian injury
outcome and provides evidence of variation in these non-vehicle factors across vehicle models.             This
confirms that the adjustment of pedestrian injury severity ratings for such factors through the logistic
regression technique is required. This adjustment ensures, as far as possible, that the pedestrian safety
ratings estimated reflect the safety performance of the vehicle model colliding with the pedestrian and are
not influenced by external, non-vehicle factors.




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7         Value of Safety Ratings for Consumers and Policy
          Makers
7.1       Studies of Consumer Behaviour in Sweden and Spain regarding
          Car Safety (ST 4.1.)
In the past decade, there has been a significant increase in the amount of consumer interest in the safety
performance of vehicles. Despite the increasing importance of vehicle safety, the role that it plays in
consumers’ purchase decisions is poorly understood.

A comprehensive literature review was undertaken to assess the current state of knowledge regarding the
role of vehicle safety in consumers’ purchase decisions. The findings of relevant market research conducted
by vehicle manufacturers, the insurance industry and university researchers indicated that vehicle safety is
important to consumers and has become more important over the past decade. Overall, the review of
literature highlighted that vehicle safety is generally not the primary consideration in the vehicle purchase
process and is consistently outranked by factors such as price, appearance and dependability/reliability. In
addition, consumers often equate vehicle safety with the presence of specific safety features or technologies.
The literature also revealed that most consumers do not seek out crash test result information specifically.
Rather, they expect safety considerations to be incorporated into the reviews and recommendations of
consumer publications that they consult.

However while the findings provide useful information on some aspects of consumers’ purchasing decisions,
it is difficult to make definitive conclusions from the literature due to the fact that there were wide variations in
the study designs, methodological limitations such as small or undefined sample sizes, and a range of
biases.

Aims of the Study

The aims of the current study were to determine, how consumers conceptualise vehicle safety, what they
understand about vehicle safety, how important vehicle safety is in the new vehicle purchase process, and
what importance they place on safety options/features relative to other convenience and comfort features.

In addition, given that a significant proportion of the new vehicle market is comprised of two purchasing
groups: individuals who purchase vehicles for private use (private vehicle purchasers) and individuals who
purchase or lease vehicles for business use (fleet vehicle purchasers), the current study aimed to investigate
the role that safety plays in the new vehicle purchase process for both groups.

Results for private purchasers

The following findings are based on responses to a questionnaire that was completed by participants from
Sweden and Spain who were the main or joint decision maker in the purchase of a new vehicle within the
past 18 months.




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How important is ‘vehicle safety’ to consumers?

One of the main aims of this research project was to try to determine how important ‘vehicle safety’ is in the
new vehicle purchase process. Previous research outlined earlier, concluded that while vehicle safety has
become increasingly important to new vehicle consumers over the past decade, it is generally not the
primary consideration in the vehicle purchase process. Indeed, when participants in the current study were
asked to select vehicle factors from a list that were a high priority in their purchase decision, participants
were more likely to select the vehicle’s reliability, comfort and fuel consumption as high priorities compared
to their vehicle’s safety (defined as the vehicle’s EuroNCAP rating). However, when participants in the
current study were asked to rank the importance of these vehicle factor priorities, most participants ranked
their vehicle’s EuroNCAP rating as the most important factor.

Similarly, when participants were asked to identify vehicle features from a list that were a high priority in their
new vehicle purchase process, participants were most likely to list vehicle features that were safety related
(e.g., advanced braking systems). In addition, when asked to rank the importance of these vehicle feature
priorities, participants were more likely to rank a safety-related vehicle feature as their number one priority
than a non safety-related feature. Furthermore, when participants in the current study were asked to list the
three most important factors that they considered when deciding which vehicle to purchase (in an open-
ended format), participants were most likely to list safety as the most important consideration, or one of their
top three considerations, compared to the vehicle’s price, design, fuel consumption etc.

Who is “vehicle safety” more important to?

Overall, the findings of the current study indicate that vehicle safety was a high priority in the purchase
process for new vehicle consumer. Another key aim of the current study was to identify factors, including
demographic characteristics (country of residence, age, gender) that may influence the importance of vehicle
safety in the new vehicle purchase process.

The findings from the current study showed that the importance of vehicle safety in the new vehicle purchase
process differed significantly for participants from Sweden compared to participants from Spain. For
example, participants from Sweden were significantly more likely to rate their vehicle’s EuroNCAP rating as a
high priority and as the highest ranked vehicle factor in the new vehicle process compared to participants
from Spain. On the other hand, while most participants from Sweden ranked their vehicle’s EuroNCAP rating
as the most important factor, most Spanish participants ranked their vehicle’s comfort as the most important
factor, with their vehicle’s EuroNCAP rating ranked equal second with their vehicle’s reliability (see Figure 30
below).




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                    Warranty                    *                                           Spain
                         Type
                         Size                                                               Sweden
                         Style                                *
                Running costs                             *
                      Re-sale
                   Reputation                         *
                    Reliability
                         Price
                 Performance                                               *
                 Make/Model
                          Fuel
                   EuroNCAP                                                                  *
                      Country
                     Comfort                                                   *
                                  0        10             20               30          40           50
                                                              Percentage



Figure 29       Factors ranked as the highest priority in the new vehicle purchase decision for Sweden and
Spain

Furthermore, when participants were asked to list the three most important factors that they considered
when deciding which vehicle to purchase, participants from Sweden were significantly more likely to list
safety as their most important consideration and as one of their top three considerations than Spanish
participants.

Table 29        Three most important factors considered by participants when purchasing their new
vehicle for Sweden and Spain
                             1st Factor                           2nd Factor                     3rd Factor
                             Sweden        Spain                  Sweden           Spain         Sweden       Spain
 Comfort                        4%             5%                   10%                5%          13%            8%
 Design/style                   3%            13%                    4%               14%          11%           11%
 Feeling when driving           1%            1%                     3%               1%            5%            1%
 Economy/value                  4%             1%                    5%                0%           4%            2%
 Engine                         0%            10%                    0%               6%            1%            6%
 Equip/Tech/Features            0%             3%                    3%                3%           3%            8%
 ESP                            0%             3%                    0%                5%           0%            5%
 Fuel consumption               7%             3%                   10%                6%           7%            2%
 Maintenance/Service            1%             0%                    3%                0%           5%            0%
 Make/model                     2%            8%                     2%               6%            1%           6%
 Performance                    0%             3%                    1%                8%           1%            6%
 Price                         14%            10%                   10%               8%           11%            9%
 Reliability                    6%             1%                    3%               1%            4%           1%
 Safety                        36%            19%                   22%               13%          12%           11%
 Size                           3%             6%                    3%                5%           3%            4%
 Space                          1%             5%                    3%                4%           3%            6%
These findings are consistent with the well-documented vehicle safety culture in Sweden.

When participants were asked to indicate the vehicle feature that they considered to be the highest ranked
priority in their new vehicle purchase process, participants from Spain were significantly more likely to list a
vehicle feature that was safety related (e.g., advanced braking systems and driver airbag) compared to




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participants from Sweden where the highest ranked priority features were not safety related (e.g., automatic
transmission and route navigation systems). These findings are surprising given the converse was true for
the priority ratings for overall safety factors for Sweden and Spain. It is possible that the priority placed on
broad safety factors by Swedish participants’ might be explained by the ‘long-held ‘ and deep-rooted safety
culture’ in Sweden. In addition, there may be an expectation by Swedish consumers that safe vehicle
features come as standard, whilst in Spain, there is a need for consumers to be more vigilant and selective
in choosing specific vehicle features that contribute to the overall safety of the vehicle. However, the
availability of such features in Spain is not clear.

Previous research has also suggested that demographic factors such as age and gender may also
significantly influence the importance of vehicle safety in the new vehicle purchase process. When the data
from both countries was pooled, older participants tended to be more likely to list ‘safety’ as their most
important consideration in the new vehicle purchase process compared to middle aged and younger
participant. In addition, female participants were more likely to list safety as their most important
consideration compared to males.

Decisions about vehicle purchases are likely to be influenced by multiple factors. Using regression analysis,
it was possible to explore the relative importance of a number of variables in determining consumers’ priority
rating assigned to safety. As tabled below, the analysis showed that vehicle safety priority was influenced by
use of EuroNCAP, gender and education level, age, drivers’ concern about crash involvement, first vehicle
purchase, annual driving distance, person for whom the vehicle was purchased, and traffic infringement
history.

            More Likely                                                          Less Likely

   Used EuroNCAP as an information source                     Purchased vehicle for spouse

   Females with a higher education (compared with             Previous traffic infringements (unbelted)
   males)

   Aged over 55 years                                         Males with a higher education (compared to
                                                              other males)

   Concerned about crashes

   Purchased 1st vehicle

   Driving more kilometres per annum

How do consumers conceptualise or understand “vehicle safety”?

In this study, participants were asked to list up to three factors that they believe make vehicles safe. Swedish
participants were most likely to list airbags and braking systems such as ABS as the most important factors
that make a vehicle safe. Spanish participants were also most likely to list braking systems, as well as
stability control systems as the most important factors that make a vehicle safe.




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                          Other                                          Spain
  Word of mouth (family/friend)                                          Sweden
  Word of mouth (professional)
              Vehicle dealership
            Motoring magazines
               Consumer reports
              Motoring websites
                     EuroNCAP
            Manufacturer website

                                   0   10   20    30    40    50    60    70     80
                                                   Percentage


Figure 30        The most valuable source of information when deciding which vehicle to purchase

It was interesting to note that even though EuroNCAP ratings were ranked as the number one priority by
Swedish participants and the number two priority by Spanish participants in terms of desirable vehicle
factors, only four percent of Swedish participants and no Spanish participants stated that their vehicle’s
EuroNCAP rating was the most valuable source of information in the new vehicle process.

Results for fleet purchasers

The following findings are based on responses to a questionnaire that was completed by Swedish and
Spanish individuals who were responsible for the fleet purchase/lease decisions of their company.

How important is ‘vehicle safety’ to Fleet Managers?

When asked to indicate the vehicle factors that are included in their company’s criteria for purchasing/leasing
a new vehicle, fleet managers from both Sweden and Spain were more likely to list the vehicle’s price,
reliability, running costs, size and fuel consumption then the vehicle’s safety (defined as the vehicle’s
EuroNCAP rating). In addition, when asked to indicate the vehicle factors that were a high priority in their
purchase/lease decision, fleet managers from both Sweden and Spain were more likely to list the vehicle’s
price and reliability as higher priorities compared to their vehicle’s safety (defined as the vehicle’s EuroNCAP
rating). Furthermore, when asked to indicate the vehicle factors that are included in their company’s policy
regarding new vehicle purchases/leases, fleet managers from both Sweden and Spain were more likely to
state that the vehicle’s price, make/model and type were included in the policy compared specifications
regarding the vehicle’s safety (defined as the vehicle’s EuroNCAP rating).

Consistent with previous research conducted with private consumers, the findings of the current study
suggest that vehicle safety is generally not the primary consideration in the vehicle purchase process and is
consistently outranked by factors such as price and dependability/reliability




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Who is “vehicle safety” more important to?

Consistent with the overall findings for the private consumers, vehicle safety appears to be more important to
Swedish fleet managers compared to Spanish fleet managers. For example, Swedish fleet managers were
more likely to state that the vehicle’s EuroNCAP rating was a high priority (see Figure 32 below) and
included in their criteria for purchasing/leasing a new vehicle (although these differences did not meet
statistical significance).

        Warranty                                                       *
             Type
              Size
             Style
    Running costs
          Re-sale                                                  *
      Reputation
        Reliability
             Price
     Performance                                                       *
     Make/Model
              Fuel                                                 *
      EuroNCAP                                                                      Spain
          Country
         Comfort                                                                    Sweden

                      0          20             40            60             80            100
                                                  Percentage


Figure 31         Factors reported as a ‘high’ priority for the company when purchasing/leasing a new vehicle2

Interestingly, there was no significant difference in the proportion of fleet managers who indicated that
EuroNCAP ratings were part of their official policy across the two countries.

How do consumers search for and use information in their purchase decisions? What information is most
important?

When asked to indicate the sources of information they used when purchasing their new vehicle, there were
several significant differences across the two countries. Swedish fleet managers were significantly more
likely to report that they used manufacturer websites, motoring websites, and EuroNCAP ratings, whereas
Spanish participants were significantly more likely to report that they used information from
professional/technical/mechanical sources.

There was also a significant difference across the two countries when fleet managers were asked to indicate
which source of information that was the most valuable to them in the pre-purchase decision. Most Swedish
fleet managers cited the vehicle manufacturer’s website as the most valuable source of information whereas
most Spanish fleet managers cited the vehicle dealerships as the most valuable source of information.



2


          EuroNCAP was listed on the questionnaire as ‘EuroNCAP rating/other safety reports’.



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Interestingly, EuroNCAP ratings were only cited by small proportion of Swedish fleet managers and no



                          Other

  Word of mouth (family/friend)

  Word of mouth (professional)

              Vehicle dealership

             Motoring magazines

               Consumer reports

               Motoring websites

                     EuroNCAP                                                  Spain
            Manufacturer website                                               Sweden

                                   0      20         40        60         80           100
                                                     Percentage

Spanish fleet managers as the most valuable source of information.

Figure 32        The most valuable sources of information used by fleet managers when purchasing/leasing a
new vehicle

This finding is consistent with the current findings for private new vehicle purchasers that crash test result
information such as EuroNCAP ratings are not the most valuable source of information in the pre-purchase
process because participants expect safety considerations to be incorporated into the reviews and
recommendations of consumer publications that they consult.

Conclusions and recommendations

The findings of the current study indicate that vehicle safety is the primary consideration in the purchase
process for private new vehicle consumers in both Sweden and Spain. Overall, participants from both
countries were most likely select safety related factor (e.g., EuroNCAP rating) and a safety-related feature
(e.g., ABS) from a list of factors and features as their highest priorities in the new vehicle process. However,
vehicle safety was significantly more important to Swedish new private vehicle consumers overall compared
to Spanish new private vehicle consumers. Consistent with previous research, most participants equated
vehicle safety with the presence of specific vehicle safety features or technologies rather than the vehicle’s
crash safety/test results or crashworthiness.

Fleet managers from both Sweden and Spain indicated that vehicle safety is not the primary consideration
when purchasing/leasing a new company vehicle. Rather, factors such as price and reliability appear to be
the highest priorities in the new vehicle purchase/lease process. Consistent with the overall findings for the
private consumers, vehicle safety appears to be more important to Swedish fleet managers in the new
vehicle purchase/lease process compared to Spanish fleet managers. Most Swedish fleet managers cited



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the vehicle manufacturer’s website as the most valuable source of information whereas most Spanish fleet
managers cited the vehicle dealerships as the most valuable source of information.

Overall, the study suggests a need to increase the profile of vehicle safety amongst both fleet managers and
private vehicle purchasers. One important way of achieving this may be to educate consumers about where
to locate objective information about vehicle safety, such as EuroNCAP. In addition, EuroNCAP needs to be
promoted more widely and effectively so that it plays a more prominent role in their new vehicle choices

For fleet managers, awareness needs to be raised about vehicle safety especially with respect to costs and
benefits for occupational health and safety in order to protect their most valuable company resources. Fleet
owners should also be encouraged to develop vehicle purchase policies that would include specific criteria
for ensuring a high level of safety in their fleet.

For private vehicle purchasers, the findings highlighted the need to target particular consumer groups in
order to increase their knowledge regarding vehicle safety and to encourage them place highest priority on
safety in the new vehicle purchase process.

Future Research

This study identified a number of interesting differences between Sweden and Spain in terms of the
importance of safety in the new vehicle purchase process. It will be important to determine whether these
findings can be generalised to other European countries, especially where there is a poor safety record.

Whilst this study has been successful in exploring the importance of vehicle safety in the new vehicle
purchase process, the findings could be enhanced by use of other survey methods such as willingness-to-
pay.


7.2       Possibilities of enhanced Consumer Information (ST 4.2.)
The objectives of this SARAC Subtask were to identify the risk of confusing consumers over non-
concordance of safety information and how safety rating information could be enhanced.

Non-Concordance of Rating Systems

The first task compared the safety ratings produced for a range of modern vehicles using two different
safety-rating systems, one using visual inspection techniques and the other, insurance crash data, with crash
test results generated through the EuroNCAP test program.

While there were a number of anomalies in the ratings using these various systems, overall, there was a
high degree of consistency too in their results. Minimising these inconsistencies is important for ensuring that
consumers embrace safety ratings as an integral part of buying a new vehicle. Such an initiative will help
promote vehicle safety more widely in the community and ensure that the current push for manufacturers to
provide ever-increasing safer new vehicles continues.




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Web-Based Computer Program

The second task undertaken in this Subtask was to outline a computer-based internet website of relevant
information with a user-friendly interface to provide ready access to up-to-date information of the benefits
and limitations of new safety technology on new cars and SUVs. In addition, the website was intended to
provide a search engine, capable of selecting particular makes and models of new cars and SUVs that
match an individual’s safety and particular purchase criteria.

Such a system was outlined in Chapter 3 of this report. It contained 3 sections: 1) access to existing
prospective and retrospective ratings of vehicles, 2) access to a range of safety feature information to inform
motorists on how these systems work, and their potential to reduce injuries and/or crashes, and 3) an
interactive system to enable purchasers to specify various vehicle-related features of importance to them
when choosing a new vehicle. The program would then list all suitable production vehicles that meet their
criteria, thereby helping to streamline their buying decision and at the same time promote safety as an
integral part of that decision.




Figure 33        Outline of the proposed SARAC Safety website

A three-level site structure was adopted for the web page. Level 1 (high level) contained a menu structure
that would allow easy navigation throughout the web site, Level 2 (mid level) to divide the search functions
into two categories: first, to search for information regarding a specific vehicle, or class of vehicles or second,
to search for vehicles based on what safety features they contain. The third level (low level) provided
information about the topic selected to be information about the topic selected would be presented to the
user presented to the user.




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              Diagram showing mid-level menu structure,          Lower level menu structure and relationship
                     with main menu header bar                               to mid-level menu.
Figure 34        Diagrams of menu structure for SARAC Safety web-site

The SARAC Concept Website page layout was designed using common web page formatting to aid
familiarity for the user. A well thought out page layout is critical to reduce visual clutter on web page and
present the information so that the reader can more effectively absorb without getting distracted.

Conclusions and recommendations

The objectives of this Subtask were to identify the risk of confusing consumers over non-concordance of
safety information and demonstrate how safety rating information could be enhanced. The results observed
for the two European comparisons generally showed a good degree of concordance between the findings
from these systems and those from EuroNCAP. In task two, a web-based system to provide consumers with
enhanced safety information was outlined, comprising access to existing prospective and retrospective
ratings of vehicles, access to a range of safety feature information to inform motorists on how these systems
work and their safety benefits, and an interactive system to enable purchasers to specify various vehicle-
related features of importance to them when choosing a new vehicle. The program would provide a list of
suitable production vehicles that meet their criteria, helping to streamline their buying decision promoting
safety as an integral part of that decision.

Recommendations

It was recommended that steps be taken to further develop the web-based system outlined in this report and
make it accessible to all new vehicle buyers in Europe. To ensure it continues to provide the latest
information on safety and other relevant new car features, it will require an ongoing commitment to regularly
update the information available. Given the diversity of safety features as standard and optional equipment in
new vehicles, it may need to take account of what country the system is directed at. In this regard, a small
pilot program would be a useful first step in its development.




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8         Recommendations and Conclusions
A number of recommendations stemming from the research program undertaken in SARAC II were noted
from the various reports, and these are listed below.


8.1       Recommendations for Improvement of Safety Ratings in Europe
1.      Develop an advanced retrospective model of safety rating for use in Europe

A uniform European safety rating system should be defined to provide vehicle crashworthiness rating results
within member countries and, more importantly, by combining the real world crash data of all or most EU
countries. Since most member countries record only injury crashes in official (Police reported) data
collections, a method based on the matched pairs of drivers involved in two-car crashes should be used.
SARAC 2 has identified that the Monash Newstead method is the most satisfactory method of this type for
rating crashworthiness, is relatively immune from variations in aggressivity of the partner car, and allows the
use of statistical methods to take into account variations in confounding factors such as the driver age and
sex.   In addition, this method enables the estimation of vehicle aggressivity, a measure not currently a
component of most European systems.

1.1     Facilitate access to national databanks for safety rating purposes

Access to national mass databases is crucial for undertaking scientific analyses of road crashes in Europe.
These data permit the extent of crash or injury across a particular region to be determined and pinpoint ways
and priorities for addressing road trauma solutions. This access is not always available and hence
researchers struggle to conduct meaningful studies of road trauma in specific regions. The EU should
encourage the data owners (usually the countries and states within Europe) to make their databases more
freely available for legitimate research projects.

1.2     Improve the level of injury severity recorded in police accident data in Europe

Police reports are commonly used for a range of safety analyses around the world. They provide a means of
establishing the extent of crashes and injury severity from car crashes and point to areas where solutions are
needed. They are particularly valuable for rating the safety of passenger vehicles in retrospective rating
systems, but the detail on injury to vehicle occupants relates almost exclusively to outcome, typically killed,
seriously injured, minor injuries, not-injured. Increasing the level of detail on actually injuries sustained (body
region injured, severity, and so on) would enable a substantial increase in the value of these data for vehicle
safety rating as well as for road safety improvement generally. Linkage to hospital records or injury insurance
claims data would be an effective way of enhancing the injury information in the police accident data, if
protocols would permit this process.

1.3     Integrate a measure of impact severity in police accident/crash data

The absence of any direct measure of impact severity in police-reported crash data is a potential major
threat to the reliability of current secondary safety rating systems in indicating relative vehicle safety. To a



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large extent, it is assumed that all makes/models have the same impact speed distribution. Most methods
use proxy measures to take into account wide disparities in the crash environment and driver characteristics,
but differences in impact speed could still be present. The SARAC projects have shown that even crude
measures of impact severity, such as vehicle damage categories, have strong association with the injury
outcomes. Measures of vehicle deformation could also be considered, depending on what can be reliably
and easily collected by reporting police officers. The availability of even the most basis measure of impact
severity in police-reported crash data would greatly improve the reliability of vehicle safety ratings and should
be implemented as soon as possible.

1.4     Collect data on vehicle registration and annual mileage by make/model of car in Europe

The need for comprehensive crash data is critical in establishing risk of crash or injury involvement in road
safety analyses. Comprehensive data on vehicle registrations and annual mileage in particular would enable
more comprehensive analyses of safety ratings (including the estimation of primary safety ratings by vehicle
make/model) and crash involvement to be undertaken, let alone other important analyses of aspects of
safety and crash risk generally.

1.5     Establish and mandate a standard European accident reporting protocol for European databases to
facilitate enhanced safety ratings (especially a tow-away entry criterion)

The reliability of secondary safety ratings is directly related to the amount of crash involvement data for each
make/model of car, especially the most recent cars and cars that are not sold in substantial volumes in any
one country in Europe. The reliability can be improved by combining the data for many countries, as was
attempted for British, French, German and Finnish crash data in SARAC 2. Such combination of data was
possible only to a very limited extent, because of the substantial differences in accident reporting protocols in
the four countries. In particular, only in Finland were non-injury crashes recorded in the official data
collection. This meant that it was difficult to estimate injury risk by make/model from the data, except in the
case of two-car crashes for which a number of methods based on double-pair comparisons have been
developed (but inapplicable to single-vehicle crashes and other important crash events). To maximise the
reliability of safety ratings, it is necessary to mandate a European-wide accident reporting protocol, desirably
with an entry criterion such as at least one vehicle towed-away in order to include non-injury crashes.

2       The EU Commission should establish a Vehicle Safety Rating Organisation to continue and
update safety-rating studies based on real world crash data using a uniform EU system in future

In the same way as the Commission supports the on-going EuroNCAP process, the Commission needs to
establish an on-going organisation to undertake safety-rating studies based on real world crash data with at
least two major objectives. The first objective is to provide safety ratings based on the full range of real crash
circumstances, to complement the EuroNCAP test results from a very limited range of circumstances, in
order to establish the generalisability of the results. The second is to evaluate the evolutionary changes in
EuroNCAP in order to ensure that they continue to be effective in improving average vehicle safety in real
world crashes. These studies would be best undertaken by a properly established Vehicle Safety Rating
Organisation using an advanced retrospective model for safety ratings based on SARAC research, applied


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to a European-wide crash database collected using a standard accident reporting protocol. The same
organisation should ultimately develop primary safety rating methods to complement the secondary safety
methods and to provide evidence of the benefits of crash avoidance technologies as fitted to individual
makes and models of cars.

2.1     Establish a continuing process for monitoring safety rating improvements

The SARAC projects have monitored the development of safety rating methods over the last decade and, as
a by-product of applying those methods to real crash data as part of the research making comparisons with
EuroNCAP, have provided results indicating real improvements in vehicle safety. The opportunity to build on
these developments would be lost unless a process of continuing this monitoring and analysis is put in place
in Europe. Understanding of the methods and existing SARAC datasets should be consolidated in at least
one institution that appreciates the knowledge developed to date and can continue the monitoring process.

3       Investigate ways of ensuring a standard VIN format in Europe and means of providing central
access to these data for enhancing safety ratings

The Subtask 1.3 report (Scully, Fildes & Logan, 2005) illustrated the large divergence in the coding of the
Vehicle Identification Number (VIN) across the various European countries. For the most part, this reflects
current practice which does not formally specify how VIN is to be structured in this region beyond the broad
ISO standard. By contrast, the USA regulators do specify this and require manufacturers to submit these
details for every car that is sold in their country and then subsequently make these data available for
legitimate users. Such a system in Europe would open the way for these data to be incorporated in rating
systems and thus, allow a more rigorous and controlled analysis to be undertaken which would yield
considerable improved ratings of vehicle crashworthiness.

3.1            Examine methods for detailed coding of safety features and model details using the VIN or
VON system

In addition to issues of harmonised coding and free availability of VIN, there are also issues related to how
best to capture details of safety features and precise model details in this number. Safety technology in
vehicles is developing at a rapid pace and the various types of features (eg: type of restraint) are difficult to
define in such a limited number. The introduction of the Vehicle Operating Number (VON) in the USA
provides greater details on these features and thus, allows optional safety equipment to be identified this
way. It would be worth considering introducing such a system in Europe to facilitate identification of safety
features in today’s fleet.

4       Fleet owners should also be encouraged to develop vehicle purchase policies that would
include specific criteria for ensuring a high level of safety in their fleet.

Vehicle fleets represent a substantial proportion of new car sales around the world. Employers throughout
Europe are expected to provide a safe working environment for their employees through Occupational
Health and Safety legislation. It could be argued that unless they purchase the safest vehicle for their




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employees to use during their business activities, they are not fulfilling their OH&S requirements. Moreover,
after 2 or 3 years, fleet vehicles usually flow on to private owners through the second-hand car market;
hence it also represents a useful way of upgrading safety generally in the used car fleet. It would be prudent
for the EU to examine ways by which employers adopt policies aimed at buying the safest vehicle affordable
for use by their employees.

4.1     Develop incentives for ensuring fleets adopt safety purchase polocies for new fleet vehicles

Fleet managers indicated that vehicle safety is not the primary consideration when purchasing/leasing a new
company vehicle. Rather, factors such as price and reliability appear to be the highest priorities in the new
vehicle purchase/lease process. Awareness needs to be raised about vehicle safety especially with respect
to costs and benefits for occupational health and safety in order to protect their most valuable company
resources. Fleet owners should also be encouraged to develop vehicle purchase policies that would include
specific criteria for ensuring a high level of safety in their fleet.

4.2     Target particular consumer groups in order to increase their knowledge regarding vehicle safety and
to encourage them place highest priority on safety in the new vehicle purchase process.

The surveys conducted under the SARAC program clearly illustrated a need for consumers in general to be
more fully aware of the need for greater safety when choosing to buy a new car. This was particularly so in a
country with a poorer safety record, but nevertheless, still of concern too in a “safe country”. Ways in which
consumers could be encouraged to focus more on safety when purchasing a new vehicle would be
worthwhile to ensure increased self-protection and enhanced safety generally among the total European
vehicle fleet.

4.3     Examine ways in which safety-rating information is promoted more widely in Europe

Several Subtasks noted the need for safety rating information to be more widely promoted and used within
Europe. Specific program to address this were suggested in these reports (fleet safety policies, web-based
internet, promotion of safety features with known safety benefits, etc). Prospective and retrospective ratings
have been shown to have a significant influence on consumer choice and development of safety technology.
There is considerable merit in examining other additional programs to ensure this trend continues and is
enhanced.

5       Establish the introduction of a web-based Internet system to promote safety through the
provision of increased safety Information within Europe.

The need for greater provision of safety information and tools to aide motorists in streamlining their buying
decisions was raised in two SARAC Subtask reports. This was considered important in encouraging
motorists to be more focussed on safety when buying a new or used motor vehicle. This would also help to
promote vehicle safety generally within Europe. The establishment and maintenance of a web-based internet
system along the lines of the model developed in Subtask 4.2 should be undertaken by the European
Community as a matter of highest priority.




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6       Encourage the widespread adoption of EDR technology in all new vehicles and its inclusion
in crash statistics for enhanced safety ratings

Event Data Recorders (EDRs) offer considerable scope for providing definitive information on crash severity,
travel speed, airbag deployment, restraint wearing, and other performance information in a crash. Currently,
these details are only available from in-depth investigations of crashes and then usually estimated, rather
than derived directly from on-board technology. The requirement for all new vehicles to have this technology
fitted would open the way for the inclusion of these details in police reports and databases and eliminate
much of the guesswork associated with estimating these parameters. This would provide a significant
advancement in crash data analysis and improve the process of safety rating substantially.

7       Investigate the need for mandating safety technology in vehicles based on superior
performance in safety rating results

The development and introduction of new safety technologies in today’s passenger cars and SUVs is rapid
and, where justified, needs to be promoted and encouraged. The European Parliament are urged to consider
the need for new initiatives aimed at ensuring safety features with known safety benefits are introduced
widely throughout Europe.

8       Develop a European fatal crash reporting system, with expansion to cover all crashes
resulting in hospital admission as soon as possible to enhance safety ratings

The Fatal Accident Reporting System (FARS) in the USA has been valuable for identifying a number of
safety initiatives related to vehicles in this country. The Insurance Institute for Highway Safety and other key
organisations in this country use these data in assessing safety performance such as crashworthiness, crash
involvement and vehicle compatibility. Introducing a similar system in Europe opens the potential for more
detailed studies to be undertaken in this area leading to new safety policies and programs. However, fatal
accident data alone has limited potential to rate the safety of cars by make and model because, at the
individual make/model level, there would (fortunately) be too few fatal crash involvements to reliably rate
their relative occupant protection. Expansion of the system to bring together data on all crashes in Europe
resulting in hospital admission as well as fatal crashes would provide a minimum dataset to form the basis of
reliable ratings. (Expansion to include all crashes resulting in at least one vehicle towed-away would provide
an even better basis.)




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8.2       Areas Requiring Future Research
9       Develop rigorous methods for rating safety of crash involvement technology based on real
world crashes

SARAC 2 has identified a wide range of technologies fitted to vehicles aimed at reducing the risk of crash
involvement (primary safety). Most of the vehicle safety rating methods investigated in SARAC 1 and 2 have
measured the risk of injury or death to occupants when their vehicles crash (secondary safety). There is now
a need to develop primary safety rating methods to complement the secondary safety methods and to
provide evidence of the benefits of crash avoidance technologies as fitted to individual makes and models of
cars. As with the secondary safety methods, these methods need to be developed rigorously to take into
account confounding factors such as the nature and extent of road use (exposure) and driver characteristics
affecting crash risk.

10      Investigate the reasons why the comparison between retrospective and prospective ratings in
Europe shows only limited correlation

In general, SARAC 2 has found only limited correlation between the score each particular make/model of car
achieves in EuroNCAP and the estimates of the risk of serious injury of drivers of the same make/model car
when involved in real world crashes. The results do, however, confirm that the design priorities encouraged
by EuroNCAP do lead to improved real world crash performance on average. The limited correlation
between the two ratings appears to be due to the different objectives of the two systems, the relatively
greater influence of car mass on the real world crash outcomes, and the relative weights given to the
EuroNCAP dummy scores from the front and side impact tests, respectively. These reasons should be
further investigated and consideration given to aligning the objectives of the two approaches to vehicle safety
rating so that they measure the same dimension in order to avoid giving conflicting information to vehicle
purchasers.

11      Develop improved methods for rating pedestrian protection in vehicles based on real world
data

SARAC 2 found virtually no correlation between the EuroNCAP pedestrian test score and the injury severity
of pedestrians injured in real world collisions with the same make/model car. The reasons for this may be
related to the selection of the EuroNCAP test sites independently of the relative frequency of pedestrians
striking these sites, or could be due to the methods used for rating pedestrian protection from real world
crash data. In practice, pedestrian crashes are recorded in official statistics only if the pedestrian is injured,
which limits the rating methods to a measure of injury severity, unlike the driver-based rating methods where
measures of injury risk and injury severity can both be calculated. An improved method of pedestrian
protection rating needs to be developed, which may include supplementing pedestrian crash data with
measures of pedestrian exposure and impact speed.




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12      The survey conducted in Subtask 4.1 should be generalised to other European countries,
especially those where there is a poor safety record.

More research effort is required in assessing consumers’ understandings and their importance of safety in
the vehicle fleet. The surveys in this research program were conducted in two select European countries and
involved a mail-out and a telephone survey methodology. It was argued that face-to-face interviews are
much preferred in this area as participants are reticent to display their ignorance and unlikely to reveal their
true values. It is recommended therefore that this research be extended in other countries using a more in-
depth approach to gain a deeper understanding of consumers’ willingness to pay for safety when purchasing
a new vehicle. This would help identify new ways of promoting safety in Europe.

13      Investigate ways to incorporate on a reliable basis the use of safety belts in the national
statistics for the better assessment of vehicle safety

Police-reported accident data may include an indicator of safety belt use, but the reliability of this data is
questionable given mandatory use requirements in all countries and police responsibilities for enforcement.
However, it is well established that safety belt use affects driver injury outcomes. The reliability of secondary
safety ratings may be compromised by the absence of this key variable for inclusion in the analysis, because
otherwise it must be assumed that drivers of each make/model of car use safety belts at the same rate.
There is a need to investigate ways to determine safety belt use in an objective way, and the most reliable
and easiest data collection should be mandated for inclusion in the national crash databases of EU
countries.




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9           Appendices


There are different categories of participating organisations in SARAC II.

Sub-Contractors:        Participants working on the basis of a sub-contract agreement where their costs
                        would be funded from the project.

Advisors:               Delivering active work/contributions to sub-tasks on the basis of a letter of interest
                        between themselves and CEA, but working on their own cost.

Observers:              Members who are not obliged to active sub-task work, but participate at meetings
                        and provide their expertise including data and any other materials.

These categories have been defined in the work sheets of the contracts, but during the project

some new partner participated and worked in additional sub-tasks at their own costs without

compensation. The Document Retrieval Information Forms of the different sub-tasks are

added in appendix 1.

The experts who have participated in the different sub-tasks are listed in appendix 2. A special

thank is given to them for their continuous work and valuable advice.




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Explanation of abbreviations

The following abbreviations are used in this annex:

BASt                    Bundesanstalt für Straßenwesen, Germany

CEA                     Comité Européen des Assurances, France

CZ                      Instituto de Investigación sobre Reparación de Vehiculos, Spain

DfT                     Department for Transport , United Kingdom

GDV                     German Insurance Association, Institute for Traffic Engineering,

                        Germany

HUT                     Helsinki University of Technology, Laboratory of Transportation Engineering,
                        Finland

IIHS                    Insurance Institute for Highway Safety, USA

ITARDA                  Institute for Traffic Accident Research and Data Analysis, Japan

IVT                     Institute of Applied Transport and Tourism Research, Germany

JARI                    Japanese Automobile Research Institute, Japan

LAB                     PSA Peugeot Citroen-Renault Laboratory for Accidentology, Biomechanics and
                        Human Behaviour, France

MUARC (BF)              Monash University Accident Research Centre, Road Safety (BF) and

MUARC (MC)              Institute for Statistical Analysis (MC), Australia

NASVA                   National Organisation for Automobile Safety and Victims Aid, Japan

VALT                    Finish Motor Insurer’s Centre, Finland

VSRC                    Loughborough University, Vehicle Safety Research Centre, UK




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9.1 Appendix 1                 List of Document Retrieval Information Forms



Sub-Task 1.1                            Document Retrieval Information

Report No.              Date                          Pages
1.1                     June 2005                     18
Title and Subtitle:
Use of Vehicle Identification Number for Safety Research
Author(s):
Scully, J., Fildes, B. & Logan, D.
Performing Organisation
 Monash University Accident Research Centre, Building 70, Monash University, VIC 3800 AUSTRALIA

 Sub-Task Participants
 Pilot                            Monash University Accident Research Centre                  Prof. Dr. Brian Fildes
 ===================              ===================================
 Advisors                         BASt, Germany
                                  VW, Germany
                                  BMW AG, Germany
                                  DaimlerChrysler, Germany
                                  Ford, Europe
                                  IIHS, USA
                                  LAB, France
 ===================              =================== ==============
                                  JARI, Japan
 Observers
                                  NASVA, Japan


Abstract
This discussion paper identifies a number of issues that need to be addressed if VIN is to be used reliably to identify safety features of
vehicles in crash databases in Europe and possibly worldwide. In countries apart from the USA, the use of VIN appears to be quite
random and at the discretion of the manufacturer and/or country in the way it gets used. Where regulated, it seems that only details
related to the manufacture location and basic vehicle characteristics are specified. In these countries, the use of VIN for identifying
safety features onboard a particular feature can only be identified with the assistance of the manufacturer involved. The USA and
Canada do mandate the use of VIN and furthermore, require manufacturers to proved these details in the National Highway Traffic
Safety Administration (NHTSA) where it is freely available on a centralised database. While the details provided are only minimal for use
in crash analysis, this system at least allows researchers to gain access to VIN independently from relying on the assistance of the
manufacturer. A number of recommendations are included so that the use of VIN for safety research     purposes can be achieved.

Keywords:

VEHICLE IDENTIFICATION NUMBER, SAFETY, RESEARCH, CRASH ANALYSIS

The views expressed are those of the author and do not necessarily represent those of CEA or any of the
participants of the SARAC committee.




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Sub-Task 1.2-1                           Document Retrieval Information

Report No.              Date                          Pages
1.2-1                   February, 2006                159

 Title and Subtitle
Updated and Extended Description of Existing Car Safety Rating Methods Based on Real-World Crash Data
 Author
Heinz Hautzinger
Performing Organisation
Institute of Applied Transport and Tourism Research (IVT), Heilbronn, Germany



Sub-Task Participants
Pilot                           IVT Heilbronn, Germany                           Prof. Dr. Heinz Hautzinger
======================================================
Sub-Contractor                  TU Braunschweig, Germany
                                GDV, Germany
                                MUARC (MC), Australia
                                HUT, Finland
                                BASt, Germany
======================================================
Advisor                         DfT, United Kingdom
======================================================
Observer                        Folksam, Sweden
                                NASVA, Japan




Abstract
As a prerequisite for description of existing car safety rating methods, a conceptual framework for measuring crashworthiness of specific
car models on the basis of real-world accident data is presented in Part A of the report. It appears that well established methodological
approaches developed in the statistical sciences, especially in epidemiology, are applicable. Various alternative injury risk concepts
suitable for car passive safety rating are discussed. Special attention is paid to measuring comparative injury risk using data on two-car
crashes only (so called matched pairs designs).

Part B of the report deals with the formal description and critical discussion of nine different car safety rating methods which have been
developed by the following institutions: Folksam Research, University of Oulu, Monash University, DETR, IIHS, HLDI, AFO and JARI.
Although considerable progress has already been made, it becomes evident that further methodological advances are necessary which
will best be achieved through close cooperation between the disciplines of engineering and statistics.

Keywords
CAR SAFETY RATING, CRASHWORTHINESS, CAR DRIVER INJURY RISK, REAL-WORLD
ACCIDENT DATA
The views expressed are those of the author and do not necessarily represent those of CEA or any of the
participants of the SARAC committee.




144
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Sub-Task 1.2-2                             Document Retrieval Information
 Report No.          Date                                 Pages
 1.2-2               February, 2006                       124
 Title and Subtitle:
 Design and Analysis of Matched Studies in Empirical Car Safety Research

 Autor(s)
 Heinz Hautzinger
 Performing Organisation

 Institute of Applied Transport and Tourism Research (IVT), Heilbronn, Germany
 Sub-Task Participants
 Pilot                              IVT Heilbronn, Germany                                      Prof. Dr. Heinz Hautzinger
 ===================                ======================================
 Sub-Contractor                     TU Braunschweig, Germany
                                    GDV, Germany
                                    MUARC (MC), Australia
                                    HUT, Finland
                                    BASt, Germany
 ===================                ======================================
 Advisors                           DfT, United Kingdom
 ===================                ======================================
 Observers                          Folksam, Sweden


Abstract
In car safety research crashworthiness indicators for cars of different make and model are calculated on the basis of empirical accident
and injury data. Since in traffic accident surveys the conditions under which the process of data collection takes place cannot be set in
advance as in an experiment, the data have to be structured after collection to control for the passive safety effects of variables other
than car model (so-called confounders). In this context matching proves to be a powerful design concept.

When the risk factor car make and model or any other determinant of driver injury status is to be assessed “matching” simply means
that the cars involved in the same two-car accident are considered as a single “matched pair” rather than two independent observations.
By matching one automatically adjusts car driver injury risk measures for confounding accident-specific variables (including unobserved
and even unobservable confounders). If the matched-pairs data are analysed using an appropriate regression model of driver injury one
can, in addition, adjust for car- and driver-specific variables.

In the statistical sciences fixed effects models are the preferred tool for analysing matched-pairs data, e.g. data from matched case-
control or matched cohort studies. It is shown that this statistical model family is also most suitable for crashworthiness studies based on
two-car accident data.

Keywords

CAR SAFETY RATING, CRASHWORTHINESS, CAR DRIVER INJURY RISK, MATCHED PAIRS
ACCIDENT DATA, FIXED EFFECTS MODELS, CONDITIONAL LOGISTIC REGRESSION MODELS


The views expressed are those of the author and do not necessarily represent those of CEA or any of the
participants of the SARAC committee.




                                                                                                                                       145
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Sub-Task 1.3                            Document Retrieval Information

Report No.              Date                          Pages
1.3                     November 2005                 162
Title and Subtitle:
Scaling Measures and Improvement of Data Collection
Author(s):
Fildes, B., Fechner, L. & Linder, A.
Performing Organisation

 Monash University Accident Research Centre, Building 70, Monash University, VIC 3800 AUSTRALIA

 Sub-Task Participants
 Pilot                            Monash University Accident Research Centre                                 Prof. Dr. Brian Fildes
 ===================              ===============================================
 Sub-Contractors                  HUT, Finland
                                  TU Braunschweig, Germany
                                  IVT Heilbronn, Germany
                                  CZ, Spain
                                  MUARC (MC), Australia
 ===================              ===============================================
 Advisors                         VW, Germany
                                  BASt, Germany
                                  BMW AG, Germany
                                  DaimlerChrysler, Germany
                                  Ford EUROPE
                                  Folksam, Sweden
                                  VALT, Finland
                                  GDV, Germany
                                  DfT, UK
                                  LAB, France
 ===================              ====================        ========================
                                  JARI, Japan
                                  NASVA, Japan
 Observers                        ITARDA, Japan
                                  Honda, Japan
                                  VSRC, United Kingdom


Abstract
This research project set out to examine a number of aspects related to scaling measures and improvement of data collection for
specifying quality criteria for the safety assessment of cars based on real-world crashes. Issues related to Event Data Decoders (EDRs),
the availability of in-depth databases, the identification of a limited range of popular cars in Europe, the so-called ‘apostle cars’, and
alternative measures of safety were discussed and a number of important findings eminated from this research for the future of rating
vehicle crashworthiness and aggressivity. It was recommended that in any future research into the Safety Rating if passenger vehicles
by SARAC, that resources be made available to trial the use of alternative measures of safety and in-depth data more fully.

Keywords:

VEHICLE IDENTIFICATION NUMBER, SAFETY, RESEARCH, CRASH ANALYSIS

The views expressed are those of the author and do not necessarily represent those of CEA or any of the
participants of the SARAC committee.




146
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Sub-Task 2.1                           Document Retrieval Information

Report No.             Date                           Pages
2.1                    March 2006                     17
Title and Subtitle:
Study of the Relationship between injury outcomes in Police reported crash data and crash barrier test results: an extended
analysis of German data
Author(s):
Delaney A, Newstead S., Cameron M.
Performing Organisation
Monash University Accident Research Centre, Building 70, Monash University, VIC 3800 AUSTRALIA

 Sub-Task Participants
 Pilot:                              Monash University Accident Research Centre                      Prof.Dr. Max Cameron
 ======================              ====================================
 Sub-contractors:                    TU Braunschweig, Germany
                                     HUT, Finland
                                     IVT Heilbronn, Germany
 ======================              ==================================
 Advisors:                           LAB, France
                                     DfT, United Kingdom
                                     Ford, Europe
                                     VALT, Finland
                                     GDV, Germany
                                     BASt, Germany
 ======================              ==================================
                                     IIHS, USA
 Observers:
                                     NASVA, Japan
                                     JARI, Japan
                                     ITARDA, Japan
                                     VSRC, United Kingdom


Abstract
The results of SARAC II sub-tasks 2.1 and 2.2 found evidence of general trend to improving average vehicle crashworthiness with
increasing EuroNCAP star ratings. To investigate whether a re-classification of vehicles in the mass crash data would result in more
definitive conclusions regarding the correlation between the two measures of vehicle safety, a re-analysis of the German mass crash
data was undertaken.    The definition of a vehicle type that can reasonably be compared with a EuroNCAP tested vehicle was
broadened using additional information in the German data that provides the model and generation of crash involved vehicles. The
results of the extended analysis of the German analysis mirror those presented in SR-248.

Keywords:

NEW CAR ASSESSMENT PROGRAM (NCAP), REAL-WORLD DATA, CRASHWORTHINESS,
CORRELATION, GERMAN DATA

The views expressed are those of the author and do not necessarily represent those of CEA or any of the
participants of the SARAC committee.




                                                                                                                               147
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Sub-Task 2.1/2.2                       Document Retrieval Information

 Report No.            Date                           Pages
 2.1/2.2               March 2006                     236
 Title and Subtitle:
 Study of the relationship between injury outcomes in police reported crash data and crash barrier test results in Europe
 and Australia
 Author(s):
 Newstead S., Cameron M., Delaney A., Watson L.
 Performing Organisation
 Monash University Accident Research Centre, Building 70, Monash University, VIC 3800 AUSTRALIA

 Sub-Task Participants
 Pilot:                             Monash University Accident Research Centre                          Prof. Dr. Max Cameron
 ======================             ===============================
 Sub-contractors:                   TU Braunschweig, Germany
                                    IVT Heilbronn, Germany
                                    HUT, Finland

 Advisors:                          LAB, France
                                    DfT, United Kingdom
                                    Ford, Europe
                                    VALT, Finland
                                    GDV, Germany
                                    BASt, Germany
 ======================             =================================
 Observers                          IIHS, USA
                                    NASVA, Japan
                                    JARI, Japan
                                    VSRC, United Kingdom


Abstract
The sub-task uses police reported crash data from Great Britain, France and Germany to estimate injury risk and injury severity
measures for European vehicles. The relationship between these measures and EuroNCAP test results is evaluated for vehicles tested
under the EuroNCAP test program prior to the commencement of the study. In addition, the correlation between EuroNCAP protocol
test results and injury outcome in real crash data from Australia and New Zealand was investigated. Sub-task 2.2 extends the analysis
of subtask 2.1 by focusing on front impact and side impact police reported crashes. This sub-task aims to evaluate the relationship
between EuroNCAP test results and injury outcome in police reported crashes for each of these crash types in Great Britain, France and
Australia and New Zealand. Results from each country point to improving average vehicle crashworthiness with increasing EuroNCAP
star rating.

Keywords:
NEW CAR ASSESSMENT PROGRAM (NCAP), CRASH BARRIER, VEHICLE TESTS, REAL-
WORLD DATA, CRASHWORTHINESS, VEHICLE OCCUPANTS, INJURIES, QUALITY
SYSTEMS


The views expressed are those of the author and do not necessarily represent those of CEA or any of the
participants of the SARAC committee.


148
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Sub-Task 2.3                           Document Retrieval Information

Report No.             Date                           Pages
2.3                    March 2006                     18
Title and Subtitle:
Use of in-depth data in comparing EuroNCAP and real-world crash results
Author(s):
Newstead S., Delaney A, Cameron M.
Performing Organisation:
Monash University Accident Research Centre, Building 70, Monash University, VIC 3800 AUSTRALIA

 Sub-Task Participants
 Pilot:                             Monash University Accident Research Centre                       Prof.Dr. Max Cameron
 Sub-contractors:                   Monash University Accident Research Centre (BF)
                                    GDV, Germany
                                    BASt, Germany
 =====================              ==============================
 Advisors:                          DfT, United Kingdom
                                    Ford, Europe
                                    LAB, France
                                    BMW, Germany
                                    Daimler Chrysler, Germany
                                    VW, Germany
                                    Folksam, Sweden


 =====================              ==============================
 Observers                          NASVA, Japan
                                    JARI, Japan
                                    VSRC, United Kingdom


Abstract
In previous SARAC work comparing the relationship between injury outcomes in real crashes and the results of EuroNCAP testing,
injury outcomes in real crashes have been assessed at the broadest level with the real crash outcome measure being an average
overall risk of death or serious injury to the vehicle driver across all body regions. Being able to make comparisons between injury
outcomes on a finer injury severity scale and by body region with results from EuroNCAP testing by body region on the crash test
dummy would allow much finer assessment of the ability of EuroNCAP to reflect real world outcomes in crashes. The aim of this sub
task was to assess the suitability of available European in-depth crash data sources for comparing real world crash outcomes by body
region and on a finer injury severity scale with results of EuroNCAP testing by body region. This study examines the suitability of two
existing European databases for in-depth analysis and summarises the data requirements for future analysis.

Keywords:

NEW CAR ASSESSMENT PROGRAM (NCAP), REAL-WORLD DATA, CRASHWORTHINESS,
CORRELATION

The views expressed are those of the author and do not necessarily represent those of CEA or any of the
participants of the SARAC committee.



                                                                                                                                  149
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Sub-Task 2.4                            Document Retrieval Information

 Report No.             Date                            Pages
 2.4                    March 2006                      44
 Title and Subtitle:
 Alternative weighting of NCAP Scores to improve the relationship to real world crashes.
 Author(s):
 Delaney A, Newstead S., Cameron M.
 Performing Organisation
 Monash University Accident Research Centre, Building 70, Monash University, VIC 3800 AUSTRALIA

 Sub-Task Participants
 Pilot:                       Monash University Accident Research Centre                          Prof.Dr. Max Cameron

 Sub-contractors:             IVT Heilbronn, Germany
                              HUT, Finland
                              TU Braunschweig, Germany
 Advisors:                    LAB, France
                              GDV, Germany
                              DfT, United Kingdom
                              Ford, Europe
 Observers:                   IIHS, USA
                              NASVA, Japan
                              JARI, Japan
                              DaimlerChrysler, Germany
                              VSRC, United Kingdom


Abstract
This sub-task has examined the potential to improve the relationship between EuroNCAP test results and injury outcome measured in
real world crashes on a vehicle by vehicle basis using logistic regression techniques. The results indicate that by re-weighting individual
components of the overall EuroNCAP score or the components of the front or side impact EuroNCAP scores the relationship between
the overall score, front impact or side impact scores and real world injury measures can be improved substantially. It is noted that the
process of re-weighting the EuroNCAP score was a data driven one and this study provides no indication of the physical mechanisms
driving the relationship between the two measures. The results indicate that immediate improvement in the correlation between real
world safety measures and EuroNCAP scores could be achieved by adjusting EuroNCAP test scores by a measure of vehicle mass.
However, any potential change to EuroNCAP must be considered in light of the aims of the program and the possible influence on
vehicle purchasing patterns. In addition, the results point to the need for more in-depth analysis of the relationship between individual
components of the front impact test and real world injury outcomes to determine whether the component measures are accurate
predictors of real world injury outcome as measured by crashworthiness, injury risk and injury severity.

Keywords:

NEW CAR ASSESSMENT PROGRAM (NCAP), REAL-WORLD DATA, CRASHWORTHINESS,
CORRELATION

The views expressed are those of the author and do not necessarily represent those of CEA or any of the
participants of the SARAC committee.



150
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Sub-Task 3.1                             Document Retrieval Information

 Report No.             Date                               Pages
 3.1                    January 2006                       164
 Title and Subtitle
 Exposure Data and Primary Safety
 Author(s)
 Räty E., Ernvall T. and Kreiss J.-P.
 Performing Organisation
 Laboratory of Transportation Engineering, Helsinki University of Technology
 Institut für Mathematische Stochastik , TU Braunschweig
 Sub-Task Participants
 Pilot                            Helsinki University of Technology                              Prof. Dr. Timo Ernvall

 Sub Contractors                  MUARC (MC)
                                  TU Braunschweig, Germany

 Advisors                         LAB, France                                                   GDV, Germany
                                  BASt, Germany                                                 VALT, Finland
                                  DfT, United Kindom                                            Ford, Europe
                                  IVT Heilbronn, Germany                                        Daimler Chrysler, Germany

 Observers                        IIHS, USA
                                  ITARDA, Japan


Abstract
The aim of the report is to present a review of primary safety risks and exposures. Availability and requirements of information needed in
research is discussed. A review of primary safety devices and technologies is presented. Statistical methods for detecting safety effects
of primary safety devices are discussed as well. Different car models have different expectation values of accidents. Risk variables
determining expected numbers of accidents can be classified in four classes: driver, vehicle, environment and administration. There are
four basic types of exposures as well: Different kinds of populations operating in the traffic, characteristics related to traffic and
transportation system, time spent in traffic and induced variables, such as number of operations and amount of experience. Some of
these exposures are measurable or detectable today but a lot of improvement in data collecting methods is needed. Numbers of loss of
control, veering off the lane and drunken driving accidents are increasing continuously. Therefore ESP, alcolock and lane departure
warning systems are the most topical devices today. Lower risks to loss of control accidents have been observed to ESP cars in many
studies. However, regardless of benefits of the devices they are not yet very popular due to slow regeneration of car fleet in several
countries.
Consumers should be advised to emphasize safety issues in their car related selections. This is possible only by making safety issues
trendy and by decreasing consumer prices of safety equipment. Setting really profitably equipments as standard equipments is also
important.
After all, the most important risk variable is the driver, who makes decisions before and during the travelling. Some proportion of the
accidents would have been prevented if the drivers would have been aware of current traffic conditions and exceptions. Several projects
targeted to develop in-vehicle information solutions are currently trying to response to the information lack.


Keywords
PRIMARY SAFETY, ACTIVE SAFETY, ACCIDENT DATA, ACCIDENT RISK, ESP

The views expressed are those of the authors and do not necessarily represent those of CEA, HUT, TU Braunschweig or
any of the participants of the SARAC 2 committee




                                                                                                                                     151
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Sub-Task 3.2                            Document Retrieval Information
Report No.              Date                           Pages
3.2                     March 2006                     100
Title and Subtitle:
A framework for assessing the relative performance of various vehicle crashworthiness estimators through data
simulation.
Author(s):
Newstead S., Delaney A., Cameron M.
Performing Organisation
 Monash University Accident Research Centre, Building 70, Monash University, VIC 3800 AUSTRALIA

 Sub-Task Participants
 Pilot:                      Monash University Accident Research Centre                       Prof.Dr. Max Cameron

 Sub-contractors:            IVT Heilbronn, Germany
                             HUT, Finland
                             TU Braunschweig, Germany
 Advisors:                   LAB, France
                             GDV, Germany
                             DfT, United Kingdom
                             Ford, Europe
                             BASt, Germany
                             Folksam, Sweden
 Observers:                  IIHS, USA
                             NASVA, Japan
                             JARI, Japan
                             DaimlerChrysler, Germany


Abstract
Currently there exist a number of methods available for use in the estimation of vehicle safety ratings. The aim of this sub-task was to
relate each of these safety rating methods to a comprehensive theoretical framework that describes the process by which the crash data
being analysed is generated.     Theoretical and data simulation undertaken in this report has been able to assess the relative
performance of six different vehicle safety rating methods, a number of them in common use internationally. Performance has been
benchmarked against a defined ideal rating measure derived from a hypothesised physical framework for the generation of observed
driver injury outcome data in motor vehicle crashes. The process of simulating crash injury outcome data using the physical framework
has also been established. The study has resulted in the methods being classified into two broad classes with the benefits and
limitations of each being clearly identified. The application of a given class of rating systems is recommended for future application in
the estimation of vehicle safety performance and recommendations for future research are also provided.


Keywords:
REAL-WORLD DATA, CRASHWORTHINESS, THEORETICAL FRAMEWORK, SIMULATION
ANALYSIS

The views expressed are those of the authors and do not necessarily represent those of CEA, HUT, TU
Braunschweig or any of the participants of the SARAC 2 committee




152
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Sub-Task 3.3                             Document Retrieval Information
 Report No.             Date                                                                                    Pages
 3.3                    January 2006                                                                            172
 Title and Subtitle
 Car occupant and fleet effects
 Author(s)
 Kari T., Ernvall T. and Räty E.
 Performing Organisation
 Laboratory of Transportation Engineering, Helsinki University of Technology


 Sub-Task Participants
 Pilot                            Helsinki University of Technology                             Prof.Dr. Timo Ernvall
 Sub Contractors                  MUARC (BF)
 Advisor                          MUARC (MC)
                                  TU Braunschweig, Germany                                      IIHS, USA
                                   CZ, Spain                                                    VALT, Finland
                                   DfT, UK
                                   Folksam, Sweden



Abstract
Driver populations, car fleets, car use and traffic cultures are changing continuously, but different way in different countries. This report
examines how do the results of car safety ratings in different countries correlate with the characteristics of changing car fleets and driver
populations. Because the ratings are always individual relative analyses regardless the chosen methods, the results present the
situation only based on the certain analysed data set. This means that it is not possible directly to compare the results even between
two consecutive ratings from two different data sets in the same country. Comparisons between different countries are still more
unreliable because of typically different methodologies and data recording systems. Of course the calculated ratings are indicative and
they give only hints about some possible tendencies and possible variables behind them. Therefore this report is focused more on
showing examples from different kind of progresses in driver and fleet distributions of different countries during the past 15 years

All car manufacturers have developed the structures, control techniques and safety restraints of their products actively. This has
increased the mass of the cars in an average with almost 200 kilograms since 1990. At the same the relative mass difference between
“large” and “small” car has diminished. New car models seem to be even 40-50 per cent safer than their predecessors some 15 years
ago. Especially strong has been the decrease of injury severity rates. Very positive issue is also that newer models are less aggressive
compared to the older ones regardless the increased average mass.

Differences between new and old models or between small and large models depend strongly on their mass and design but also their
different driver and owner populations, different mileages, distributions between urban and rural use. With time the driver populations
and the use by car models change very different way, which reflects also to the fleet level and may cause remarkably pronounced
values for individual models.

Keywords:

OCCUPANT EFFECT, FLEET EFFECT, ACCIDENT RISK, INJURY RISK, SAFETY RATINGS,
MILEAGE

The views expressed are those of the authors and do not necessarily represent those of CEA, or any of the
participants of the SARAC 2 committee




                                                                                                                                        153
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Sub-Task 3.4                            Document Retrieval Information
Report No.              Date                           Pages
3.4                     March 2006                     172
Title and Subtitle:
Analysis of pedestrian crash data from Great Britain, Germany and France
Author(s):
Delaney A, Newstead S., Cameron M.
Performing Organisation
Monash University Accident Research Centre, Building 70, Monash University, VIC 3800 AUSTRALIA

 Sub-Task Participants
 Pilot:                    Monash University Accident Research Centre                     Dr. Amanda Delaney


 Sub-contractors:          HUT, Finland
                           TU Braunschweig, Germany
                           GDV, Germany
                           BASt, Germany


 Advisors:                 LAB, France
                           DfT, United Kingdom


 Observers:                NASVA, Japan
                           JARI, Japan
                           ITARDA, Japan
                           Daimler Chrysler, Germany
                           VALT, Finland
                           Honda, Japan


Abstract
The analysis conducted in this sub-task explores the feasibility of assessing pedestrian injury outcomes as a function of the colliding
vehicle model using real crash data sources from Great Britain, France and Germany.             In addition, the analysis considers the
relationship between the estimated real crash pedestrian safety ratings and EuroNCAP pedestrian star ratings. It is concluded that
there is no evidence of a relationship between the relevant EuroNCAP pedestrian star ratings and the pedestrian injury severity
estimates generated from real world crash data from the three countries. Potential factors that may contribute to the lack of relationship
between the two measures of injury outcome are explored.

The second component of the study is a descriptive analysis of pedestrian crash data from Great Britain, France and Germany. The
analysis examines the distribution of crashes across vehicle models by road and demographic characteristics that influence pedestrian
injury outcome. The results demonstrate that there is evidence of variation in these non-vehicle factors across vehicle models

Keywords:

PEDESTRIAN, NEW CAR ASSESSMENT PROGRAM (NCAP), REAL-WORLD DATA, INJURY
SEVERITY, CORRELATION

The views expressed are those of the authors and do not necessarily represent those of CEA, or any of the
participants of the SARAC 2 committee



154
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Sub-Task 4.1                            Document Retrieval Information
Report No.              Date                          Pages
4.1                     March 2006                    117
Title and Subtitle:
How important is ‘vehicle safety’ in the new vehicle purchase process?
Author(s):
Koppel, S., Charlton, J.L., Fildes, B.N., Fitzharris, M., Clark, A. , Kullgren, A., Olona Solano, A., Mäkitupa, S., Ernvall, T.
Performing Organisation
 Monash University Accident Research Centre, Building 70, Monash University, VIC 3800 AUSTRALIA

 Sub-Task Participants
 Pilot                           Monash University Accident Research Centre                 Prof. Dr. Brian Fildes
 Sub-Contractors                 CZ, Spain
                                 Folksam Insurance, Sweden
                                 HUT, Finland

 Advisors                        VW, Germany                                               LAB, France
                                 BMW AG, Germany                                           IIHS, USA
                                 DaimlerChrysler, Germany                                  BASt, Germany
                                 Ford, Europe
 Observers                       JARI, Japan                                                NASVA, Japan


Abstract
The main aim of this research was to determine the importance of vehicle safety in the process of purchasing a new motor vehicle.
Surveys were conducted in Sweden and Spain with private vehicle purchasers and fleet managers. The findings indicate that vehicle
safety was a high priority in the new vehicle purchase process. A number of factors were found to influence purchasing decisions,
including country of residence, age, driving distance, gender and education, infringement history, reason for purchasing the new vehicle,
and use of EuroNCAP ratings. The findings highlighted the need to educate particular target groups of consumers about vehicle safety
in the new vehicle purchase process. In addition, EuroNCAP results need to be promoted more widely and effectively so that they play a
more prominent role in their new vehicle choices.

Keywords:

VEHICLE SAFETY, PURCHASING DECISIONS, SAFETY RATINGS

The views expressed are those of the authors and do not necessarily represent those of CEA, or any of the
participants of the SARAC 2 committee




                                                                                                                                    155
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Sub-Task 4.2                           Document Retrieval Information

Report No.             Date                             Pages
4.2                     November 2005                   162
Title and Subtitle:
Conflicting Ratings and Enhanced Consumer Information
Author(s):
Fildes, B., Clarke, A. & Langford, J.
Performing Organisation
 Monash University Accident Research Centre, Building 70, Monash University, VIC 3800 AUSTRALIA

 Sub-Task Participants
 Pilot           Monash University Accident Research Centre                                 Prof. Dr. Brian Fildes

 Sub Contractor       HUT, Finland
                     TU Braunschweig, Germany
                      MUARC (MC)
                      CZ, Spain

 Advisors            VW, Germany
                     IIHS, USA
                      BMW AG, Germany
                      Ford, Europe
                      Folksam Insurance, Sweden
                      GDV, Germany
                      DfT, United Kingdom


Abstract
This report set out to briefly review anomalies in ratings across one prospective and two retrospective systems to examine the extent of
the variable information provided to consumers by these different systems. The findings showed overall good concordance between
systems, although there were a number of instances where ratings differed across these systems. A second task was to outline a
computer-based web system to readily provide safety information for consumers as well as software to assist new vehicle buyers in their
choice of a new vehicle with an emphasis on its crashworthiness and crash avoidance characteristics. Such a system is described here
and it is recommended that resources be provided to develop and introduce this system and maintain its accuracy and usefulness for
safety choice and promotion within Europe.

Keywords:

VEHICLE IDENTIFICATION NUMBER, SAFETY, RESEARCH, CRASH ANALYSIS

The views expressed are those of the authors and do not necessarily represent those of CEA, or any of the
participants of the SARAC 2 committee




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9.1       Appendix 2           Members in the Subtask Working Groups

Table 31: List of Sub-Tasks and participants in Working Group 1 (Working Group Leader – Professor Fildes)

Sub-Task     Activity                                 Pilot

1.1          Use of vehicle identification number     Professor Fildes (MUARC)
             for safety research

             Participants
             Claus-Henry Pastor, Yves Page, Cyril Chauvel, Robert Zobel, Klaus Schmelzer, Horst
             Roesler, Falk Zeidler, Brian O’Neill, Yuji Ono, Kazunori Mashita, Minoru Sakurai, Paul Fay.

1.2          Updated and extended Description         Professor Hautzinger (IVT)
             of   existing   Car    Safety   Rating
             Methods based on Real World
             Crash Data

             Participants
             Claus-Henry Pastor, Jens-Peter Kreiss, Lothar Schueler, Max Cameron, Stuart Newstead,
             Klaus Langwieder, Timo Ernvall, Valerie Davies, Anders Kullgren, Anders Ydenius, Pekka
             Sulander, Horst Bierau, Karin Mayer

1.3/4.3      Improvement of Data Collection           Professor Fildes (MUARC)
             and Scaling Measures/ Feasibility
             of in-depth data use

             Participants
             Yves Page, Cyril Chauvel, Robert Zobel, Josef Haberl, Klaus Schmelzer, Falk Zeidler,
             Raimondo Sferco, Brian O’Neill, Yuji Ono, Kazunori Mashita, Minoru Sakurai, Heinz
             Hautzinger, Jens-Peter Kreiss, Lothar Schueler, Max Cameron, Stuart Newstead, Klaus
             Langwieder, Timo Ernvall, Valerie Davies, Anders Kullgren, Anders Ydenius, Pekka
             Sulander, Amanda Delaney, Ana Olona, Paul Fay, Klaus-Henry Pastor, Matthew Bollington
             Thomas Hummel, Kalle Parkkari, Shunsuke Sumida, Kei Takeuchi, Kris Van der Plas,
             Andrew Morris, James Lenard, Mariano Bistuer, Lasse Hantula, Horst Bierau.




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Table 32: List of Sub-Tasks and participants in Working Group 2 (Working Group Leader – Professor
Cameron)

Sub-       Activity                               Pilot
Task

2.1/2.2    Study of the relationship between      Professor Cameron (MUARC)
           injury outcomes in police reported
           crash data and crash barrier test
           results in Europe and Australia

           Participants
           Claus-Henry Pastor, Yves Page, Page, Cyril Chauvel, Raimondo Sferco, Brian O’Neill, Yuji
           Ono, Minoru Sakurai, Heinz Hautzinger, Jens-Peter Kreiss, Lothar Schueler, Stuart Newstead,
           Klaus Langwieder, Timo Ernvall, Valerie Davies, Pekka Sulander, Amanda Delaney, Esa
           Raety, Timo Kari, Paul Fay, Kalle Parkkari, Shunsuke Sumida, Kei Takeuchi, Andrew Morris,
           James Lenard, Horst Bierau.

2.3        Examination of NCAP measures and       Professor Cameron (MUARC)
           real world data by body regions

           Participants
           Brian Fildes, Claus-Henry Pastor, Yves Page, Cyril Chauvel, Robert Zobel, Klaus Schmelzer,
           Falk Zeidler, Raimondo Sferco, Yuji Ono, Minoru Sakurai, Stuart Newstead, Anders Ydenius,
           Amanda Delaney, Paul Fay, Thomas Hummel, Kei Takeuchi, Andrew Morris, James Lenard.

2.4        Comparison between NCAP and real       Professor Cameron (MUARC)
           world crash results

           Participants
           Falk Zeidler, Raimondo Sferco, Brian O’Neill, Yuji Ono, Minoru Sakurai, Heinz Hautzinger,
           Jens-Peter Kreiss, Lothar Schueler, Stuart Newstead, Klaus Langwieder, Timo Ernvall, Valerie
           Davies, Amanda Delaney, Paul Fay, Kei Takeuchi, Andrew Morris, James Lenard.




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Table 33: List of Sub-Tasks and participants in Working Group 3 (Working Group Leader – Professor
Ernvall)

Sub-       Activity                                   Pilot
Task

3.1        Exposure data and primary safety           Professor Ernvall (HUT)


           Participants
           Claus-Henry Pastor, Yves Page, Falk Zeidler, Brian O’Neill, Heinz Hautzinger, Lothar Schueler,
           Max Cameron, Klaus Langwieder, Valerie Davies, Esa Raety, Timo Kari, Paul Fay, Kalle
           Parkkari, Shunsuke Sumida.


3.2        Framework for assessing the relative       Professor Cameron (MUARC)
           performance         of           vehicle
           crashworthiness estimators through
           data simulation


           Participants
           Claus-Henry Pastor, Brian O’Neill, Heinz Hautzinger, Jens-Peter Kreiss, Lothar Schueler, Timo
           Ernvall, Valerie Davies, Anders Kullgren, Esa Raety, Timo Kari, Kalle Parkkari.



3.3        Car occupant and fleet effects             Professor Ernvall (HUT)



           Participants
           Brian Fildes, Brian O’Neill, Jens-Peter Kreiss, Lothar Schueler, Valerie Davies, Anders
           Kullgren, Amanda Delaney, Ana Olona,
           Kalle Parkkari, Mariano Bistuer, Lasse Hantula.

3.4        Pedestrian accidents                       Ms Delaney (MUARC)


           Participants
           Claus-Henry Pastor, Yves Page, Cyril Chauvel, Falk Zeidler, Yuji Ono, Minoru Sakurai, Jens-
           Peter Kreiss, Klaus Langwieder, Timo Ernvall, Thomas Hummel, Shunsuke Sumida, Kei
           Takeuchi, Kris Van der Plas, Tomiji Sugimoto, Lasse Hantula, Horst Bierau.




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Table 34: List of Sub-Tasks and participants in Working Group 4 (Working Group Leader – Professor
Langwieder)

Sub-       Activity                                       Pilot
Task

4.1        Influence    of    safety      aspects   on    Professor Fildes (MUARC)
           consumers’        new   car     purchasing
           behaviour

           Participants

           Claus-Henry Pastor, Yves Page, Cyril Chauvel, Robert Zobel, Klaus Schmelzer, Falk Zeidler,
           Raimondo Sferco, Brian O’Neill, Minoru Sakurai,            Timo Ernvall, Anders Kullgren, Anders
           Ydenius, Ana Olona, Mariano Bistuer, Judith Charlton, Sjaanie Koppel.

4.2        Conflicting ratings and enhanced               Professor Fildes (MUARC)
           consumer information

           Participants

           Robert Zobel, Klaus Langwieder, Valerie Davies, Anders Kullgren, Amanda Delaney, Ana
           Olona, Kalle Parkkari.

4.3        See Subtask 1.3

4.4        Recommendations          for     legislative   Professor   Langwieder    (CEA),    Professor   Fildes
           actions                                        (MUARC),    Professor     Cameron    (MUARC)      and
                                                          Professor Ernvall (HUT)




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