Project Deliverables D10.0 by joq12180


									Project Deliverables: D10.0
Integrated Assessment and DSS

Programme name:          Energy, Environment and Sustainable Development

Research Programme:      1.1.4. - 4.4.1, 4.1.1

Project acronym:         SUTRA

Contract number:         EVK4-CT-1999-00013

Project title:           Sustainable Urban Transportation

Project Deliverable:     D10.0: Integrated Assessment and DSS

Related Work             WP 10 Tool Integration, multi-criteria DSS

Type of Deliverable:     RE Technical Report

Dissemination level:     RES Restricted

Document Author:         Kurt Fedra, ESS

Edited by:               Kurt Fedra, ESS

Reviewed by:

Document Version:        1.2 (Final DRAFT)

Revision history:

First Availability:      2002 01 16

Final Due Date:          2002 07 31

Last Modification:       2003 06 17

Hardcopy delivered to:   Eric Ponthieu DG XII-DI.4 (SDME 4/73)
                         Rue de la Loi, 200 B-1049 Brussels, Belgium
SUTRA EVK4-CT-1999-00013                         D10.0 Integrated Assessments

SUTRA uses a cascade of simulation models to represent the individual
scenarios of urban development. A set of common and city specific scenarios
is defined in D11. The core of the modeling system is a transportation model
(VISUM, D03) that describes an equilibrium-based solution to satisfy the
transportation demand expressed in an origin-destination matrix given a
transportation network and its capacities and constraints.

The scenario, and in particular transportation demand and the market
penetration of alternative transportation technologies are estimated with an
energy systems optimisation model, MARKAL (D07).

Based on the model results, indicators of the performance, efficiency, and
impacts of satisfying the transportation demand are generate the describe
each scenario for the final comparative analysis (D13).

These steps include:

          1. Emission calculation using the TREM model
          2. Environmental impacts (air quality):
                o VADIS, a street canyon model, that generates maximum
                   concentration estimates for selected hot-spots;
                o AirWare, a city level Gaussian air quality model describing
                   the near-field around the entire transportation network;
                o OFIS, a regional ozone model describing seasonal
                   standard violations in an around the city area.
                          Population exposure (derived from the AirWare
                          spatial results);
                          Public health impacts (derived from the air quality
                          models), calculated with an expert system based
                          on fuzzy set methodology (D09).

The resulting set of indicators (see Deliverable D8.0) are then used in a multi-
criteria analysis (D13) to define sets of non-dominated solutions, which are
also compared in a benchmarking exercise (D14) with a much larger set of
cities across Europe.

This report describes the specific discrete multi-criteria decision-support
methodology used, and the city level air quality model and population
exposure module.

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Table of Contents

EXECUTIVE SUMMARY ......................................................................................... 2
Table of Contents .......................................................................................................... 3
1. Introduction .................................................................................................................. 4
2. DSS Methodology ................................................................................................... 5
3. The user interface ................................................................................................. 11
4. Air quality modeling and population exposure.................................. 12

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1. Introduction
A central step of the SUTRA approach is the integrated assessment: this aggregates
the plethora of information obtained by these individual analytical methods into the
basic set of indicators defined in the first phase.
To make this procedure unambiguous and, if not objective so at least reproducible
and open for inspection, a rule-based approach to classification is used that directly
translates the data resulting from the analytical methods and models into indicator
The results from the scenario analysis are characterised by a complex structure that
includes not only symbolic (ordinal and nominal) and cardinal scalar variables, but
time series of scalars, networks, matrices and tensors from the simulation exercises.
This very large and 5 dimensional volume of data is extremely difficult to analyse in a
comparative way when numerous criteria or dimensions have to be considered
simultaneously (Fedra, 1999).
The resulting set of indicators describing the alternative scenarios for the set of cities
involved are now subjected to two separate but related evaluation steps:

   1. A benchmarking exercise, similar to the initial benchmarking for the base line,
      but now comparing the baseline with the future development scenarios to
      determine trends in the indicator based assessment;
   2. A multi-criteria optimisation approach based on reference point optimisation
      and a normalizing concept of distance from Utopia; the discrete multi-criteria
      method first filters the pareto-optimal subset by eliminating dominated

The set of final, optimal solutions, each for a given set of preferences (expressed as
a reference point or implicitly defined by the Utopia point of the Pareto subset) is then
obtained by measuring (with any one of a number of distance functions in the N
dimensional decision space) distance from Utopia and the reference point,
The analysis of this set of preferred scenarios for each case and set of assumptions
is expected to yield new and valuable insight into strategies towards sustainable
urban transportation.

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2. DSS Methodology
Decision support is a very broad concept, and involves both rather descriptive
information systems, that just demonstrate alternatives, as well as more formal
normative, prescriptive optimization approaches that design them. Any decision
problem can be understood as revolving around a choice between alternatives, in
this case scenarios of urban development and transportation policy.

These alternatives are analyzed and ultimately ranked according to a number of
criteria by which they can be compared; these criteria are checked against the
objectives and constraints (our expectations), involving possible trade-offs between
conflicting objectives. An alternative that meets the constraints and scores highest on
the objectives is then chosen. If no such alternative exists in the choice set, the
constraints have to be relaxed, criteria have to be deleted (or possibly added), and
the trade-offs redefined.

However, the key to an optimal choice is in having a set of options to choose from
that does indeed contain an optimal solution. Thus, the generation or design of
alternatives is a most important, if not the most important step. In a modeling
framework, this means that the generation of scenarios must be easy so that a
sufficient repertoire of choices can be drawn upon.

The selection process is then based on a comparative analysis of the ranking and
elimination of (infeasible) alternatives from this set. For spatially distributed and
usually dynamic models -- natural resource management problems most commonly
fall into this category -- this process is further complicated, since the number of
dimensions (or criteria) that can be used to describe each alternative is potentially
very large. Since only a relatively small number of criteria can usefully be compared
at any one time (due to the limits of the human brain rather than computers), it seems
important to be able to choose almost any subset of criteria out of this potentially very
large set of criteria for further analysis, and modify this selection if required.

2.1 DSS approach

In SUTRA, the decision support approach chosen is primarily constrained by the
characteristics of the underlying system. These are primarily the complexity of
scenarios, with indicators derived from a range of quite different models as well as
policy level scenario assumptions; The models themselves are spatially distributed,
with spatial resolution ranging from the street level (meters) to the regional air quality
grid (km).

These problem characteristics preclude any straight forward optimization approach.

Consequently, SUTRA uses an approach centered on

   •   Scenario Analysis
   •   comparative evaluation of scenarios
   •   discrete multi-criteria optimization.

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SUTRA EVK4-CT-1999-00013                          D10.0 Integrated Assessments

2.2 Scenario Analysis

In a DSS framework, Scenario Analysis supports the user to explore a number of
WHAT -- IF questions. The scenario is the set of initial conditions and driving
variables (including any explicit decision variables) that completely characterize the
system behavior, which is expressed as a set of output or performance variables;
both decision variables and performance variables are expressed as indicators
(defined in WP 08); the specific scenario defined in terms of these indicators are
described in WP 11.

2.3 Decision Variables

The decision parameters the user can set to define a scenario (D11) are defined and
described in detail in D08a.

All parameters derived from Indicators (D08a) are represented by Descriptors which
are terms used in the expert systems knowledge base. Descriptors are Objects that
have several Methods available to determine or update their value in a given context
(the scenario). One such method is to ask the user through an interactive dialog box.

The syntax of a descriptor definition is as follows:

   A <alias_for_descriptor_name>
   T <descriptor_type>
   U <unit>
   V <range> / <range> / <range> / ...
   R <rule#> / <rule#> / ...
   TB <table#> / <table#> / ...
   F <function>
   IF <interface function>
   G <gis_function> <gis_overlay>
   Q <question>
   T <model_type>
   I <input_descriptor> / <input_descriptor> /
   O <output_descriptor> / <output_descriptor> /
   <alternative defs>
   X <window x-coordinate>
   Y <window y-coordinate>
   WIDTH <window width>
   HEIGHT <window height>

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   BGCOLOR <window bgcolor>
   BORDER_WIDTH <window borderwidth>
   BORDER_COLOR <window bordercolor>
   FORMAT <value selector format_string>
   DELTA <value selector increment>
   HYPER_INFO <hyperinfo path>
   HYPER_X <hyperinfo x-coordinate>
   HYPER_Y <hyperinfo x-coordinate>
   HYPER_WIDTH <hyperinfo width>
   HYPER_HEIGHT <hyperinfo height>
   HYPER_TWIDTH <hyperinfo backgroundwin width>
   HYPER_THEIGHT <hyperinfo backgroundwin height>
   HYPER_FGCOLOR <hyperinfo foreground color>
   HYPER_BGCOLOR <hyperinfo background color>
   HYPER_KEYCOLOR <hyperinfo keyword color>
   HYPER_HIKEYCOLOR <hyperinfo highlight color>
   HYPER_SWBORDERC <hyperinfo border color>

A concrete example used for one of the relative (percentage) scaling factors would
V very_small[ 0, 10, 25]
V small [ 25, 50, 75]
V average [ 75,100,125]
V high     [125,200,250]
V very_high [250,300,400]
Q What is the relative change, expressed in % of the
Q original value, that you want to apply to the
Q for this scenario run ?

In this example, the possible change is constrained between 0 (switching the value
off completely) to 400, or a maximum of a fourfold increase.
A more complete and comprehensive definition of the Knowledge Base syntax for the
expert system used can be found in

2.4 Performance Variables

The performance variables measure the overall behavior of the system (in terms of a
set of partly implicit and partly explicit objectives) in an aggregate form. This is clearly
necessary for simple reasons of cognitive limitations: a scenario run of 18 hours, at
15 minutes output intervals, for a network with 2,000 links and a model domain of
10,000 cells, given only 5 link specific and 3 environmental parameters, will produce
a total of 2,160,000 data items. For comprehension (as an elementary step toward

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comparative evaluation) they must be summarized in a few performance variables.
These could include:

   •   compliance with environmental standards (yes/no) (for example, Council
       Directive 92/72/EEC on air pollution by ozone:
           o Health protection threshold
               0.110 mg/m3 for the mean value over eight hours
           o Vegetation protection threshold
               0.200 mg/m3 for the mean value over one hour
               0.065 mg/m3 for the mean value over 24 hours
           o Population information threshold
               0.180 mg/m3 for the mean value over one hour
           o Population warning threshold
               0.360 mg/m3 for the mean value over one hour
   •   average, maximum, and several spatio-temporal integrals of immission values,
   •   population exposure (spatio-temporally integrated)
   •   or an arbitrary spatially integrated (summed over n land parcels or model grids
       cells) environmental impact function of the type:

   •   where Ci represents the estimated immission in land parcel (grid cell i), Co is a
       reference or no-effects threshold, and Wi represents a landuse-dependent
       weight or penalty. The coefficients a and b describe the dose-effect behaviour
       of the pollutant in question.
   •   total traffic volume (car*km, person*km, tons*km)
   •   average speed, deviation from some reference/optimal speed
   •   average/total travel times, deviations from some reference optimal duration
   •   specific emissions (per person and ton*km).

Depending on the performance constraints in a given implementation of SIMTRAP
(computation versus communication), an appropriate implementation of the
computation, display and analysis of the performance variables will be configured on
a case by case basis.

2.5 Visualization

An additional important function provided by the user interface is the visualization of
the scenario parameters. Due to the relatively large number (consider, for example,
individual construction sites), graphical and symbolic representation is used to
summarize numerous, and in particular spatially distributed, data.

In summary, simple scenario analysis results in a single (set of) result(s), that is
(implicitly or explicitly) compared against a set of (absolute) objectives (expectations)
and constraints such as environmental standards or some minimal requirements for
average traffic flow.

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2.6 Comparative Evaluation

Comparative evaluation requires that the performance variables of more than one
scenario (minimally two for direct pairwise comparison) are displayed to the user
simultaneously. For the spatially distributed (network or domain-grid specific data)
this is accomplished by displaying equivalent data sets in two or four parallel
windows. For the performance variables, this is accomplished by the parallel display,
tabular and graphical, of the respective values.

In both cases, graphical and numerical, the side-by-side display can be augmented
by the calculation and display of relative and absolute differences (deltas) of the
respective scenario performance variables, for example, as a map of differential
(increases and decreases) immission from two ozone concentration maps
representing two separate traffic scenarios.

In summary, comparative scenario analysis results in direct comparison of two (or a
set of) result(s), that are explicitly compared against each other and interpreted in
terms of improvement or deterioration of performance variables vis a vis the
objectives and constraints.

2.7     Discrete Multi-Criteria Optimization

Since each scenario is described by more than one performance variable or criterion,
the direct comparison does not necessarily result in a clear ranking structure:
improvements in some criteria may be offset by deterioration in others. This can only
be resolved (and resulting in an eventual ranking and selection) through the
introduction of a preference structure that defines the trade-offs between objectives.

The basic optimization problem can be formulates as:


is the vector of decision variables (the scenario parameters), and

defines the objective function. Xo defines the set of feasible alternatives that satisfy
the constraints:

In the case case of numerous scenarios with multiple criteria, we can define the
partial ordering

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where at least one of the inequalities is strict. A solution for the overall problem is a
Pareto-optimal solution:

As an overall decision support tool, we can now use a discrete multi-criteria approach
to find an efficient strategy (scenario) that satisfies all the actors and stake holders
involved in the traffic and environmental management decision processes.

The preferences of decision makers can conveniently be defined in terms of a
reference point, that indicates one (arbitrary but preferred) location in the solution
space. Normalizing the solution space in terms of achievement or degree of
satisfying each of the criteria between nadir and utopia allows us to find the nearest
available Pareto solution efficiently by a simple distance calculation.

Since decision and solution space are of relatively high dimensionality, the direct
comparison of a larger number of alternatives becomes difficult in cognitive terms.
The data sets describing the scenarios can be displayed in simple scattergrams,
using a user defined set of criteria for the (normalized) axes. Along these axes,
constraints in terms of minimal and maximal acceptable values of the performance
variable in question can be set, leading to a screening and reduction of alternatives.

As an implicit reference point, the utopia point can be used. Consequently, and
unless the user overrides this default by specifying and explicit reference point, the
system always has a solution (the feasible alternative nearest to the reference point)
that can be indicated and highlighted on the scattergrams and in a listing of named

In parallel, graphical representation of the spatially distributed parameters can be
shown as thematic maps. The visualization tools based on GIS and multi-media
formats support a more intuitive and holistic understanding of alternatives that aids
the definition of a reference point and thus supports the decision making process.

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3. The user interface

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4. Air quality modeling and population exposure
A key element in the modeling cascade and for the final assessment is the city level
air quality modeling; conceptually, this is placed between the VADIS street canyon
model and the OFIS regional ozone model.
The model uses the output from TREM (and for further assessment other segment
attributes from VISUM) as its input, together with a meteorological scenario (single
event or a seasonal or annual meteo-event frequency data set derived from hourly
observations). For the primary assessment (population exposure), a spatially
distributed population data set is also used.
The model uses a simple steady-state Gaussian solution along each of the street
network segments, with a mixing zone approach for the immediate near-field, and a
convolution methodology that makes it possible to solve an entire city street network
with thousands of segments in a few seconds with a high resolution (10 m).
The result is a color-coded display of the ambient concentrations, superimposed on
the city map as well as basic statistics such as average ambient concentration in the
domain, non-zero average, maximum value, and the area (as percentage) above a
user defined threshold concentration value to facilitate scenario comparison.
An interactive read-back function shows the concentration estimates at arbitrary
locations that user can specify with the mouse pointer.
Population exposure determines the absolute and relative population size in areas
where a user defined threshold is exceeded in the current emission and meteo

      Scenario selection (left) and setting of scenario specific meteo and display
                                   parameters (right).

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             Basic model results (left) and population exposure (right).

Modifying display parameters (exposure threshold, left) and concentration read-back

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5. References and selected bibliography

Fedra, K. (2002)
      AirWare: an urban and Industrial Air Quality Assessment and Management
      Information System. In: MoussiopoulosN, and Karatzas, K. [eds], SATURN-
      EURASAP, Urban Air QUality Management Systems. 73-91 pp., EUROTRAC-
      2, ISS, Munich.
Fedra, K., and Gordov, E. (2000)
      Integrated System for Intelligent Regional Environmental Monitoring and
      Management: ISIREMM. pp 141-145. In: Gordov, E. [ed.:] Proceedings,
      ENVIROMIS'2000, 24-28 October 2000, Tomsk. Institute of Atmospheric
      Optics, SB RAS.
Fedra, K. (2000)
      Environmental Decision Support Systems: A conceptual framework and
      application examples. Thése prèsentèe á la Facultè des sciences, de
      l'Universitè de Genéve pour obtenir le grade de Docteur és sciences, mention
      interdisciplinaire. 368 pp., Imprimerie de l'Universitè de Genéve, 2000.
Fedra, K. (2000)
      Model-based decision support for integrated urban air quality management. In:
      Longhurst, J.W., Elsom, D.M. and Power, H. [eds.] Air Quality Management,
      pp 243-260, WIT Press, Southampton.
Fedra, K. (2000)
      Environmental Information and Descision Support Systems.
      Informatik/Informatique 4/2000, pp. 14-20.
Fedra, K. (2000)
      Urban environmental management: monitoring, GIS and modeling.
      Computers, Environment, and Urban Systems 23(1999) 443-457.
Fedra, K., Haurie, H. (1999)
      A decision support system for air quality management combining GIS and
      optimization techniques. Int. J. Environment and Pollution Vol.12, Nos.2/3,
      1999 , 125-146.
Zhao, Ch., Winkelbauer, L. and Fedra, K. (1985)
      Advanced Decision-oriented Software for the Management of Hazardous
      Substances. Part VI. The Interactive Decision-Support Module. CP-85-50,
      International Institute for Applied Systems Analysis, A-2361 Laxenburg,
      Austria. 39p.

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