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(IJCSIS) International Journal of Computer Science and Information Security,
Vol. 8, No. 2, 2010
AccSearch: A Specialized Search Engine for Traffic
Analysis
K. Renganathan B. Amutha
Computer Science and Engineering Department Computer Science and Engineering Department
SRM University SRM University
India India
ranganath73@gmail.com bamutha62@gmail.com
Abstract— AccSearch is a specialized web search engine to URL result for any queries pertaining to the road accident
provide information about road accidents within Chennai, details within the Chennai city. It is aimed to provide a high
India and assist the traffic authorities, police, NGOs, dependency to the user. It covers the entire accident data
lawyers, students and statistical bureaus. The people who which occurred in the four National Highways around Chennai.
are in need of road accident information for various It will provide information about the accident occurred in the
reasons are very much struggling to collect the correct day and night around the highways. This information is used
information under a single search. Special purpose search to do the historical collection of data. This traffic search
engines are designed to work on a particular domain engine can be later connected to the all purpose search engines
which fill the gap where an all purpose search engine to add up the searching power and efficiency.
lacks. As the existing search engines cannot do the traffic
search alone well for several reasons, we have designed a Markov chain algorithm is used to improve the performance
search algorithm using Markov chain, to provide the and speed up. Markov chains are well known for the
search information in a faster manner. The mathematical performance tuning and prediction.
proof of our modified Markov chain algorithm shows that
the speed and efficiency seems to be better in comparison Adding the information with the available information on
with the existing search algorithms. As Markov chain can the internet is the fruit of this work. By providing some more
be used for prediction purposes, our search engine information with the already available information some
concentrates on one particular domain which is traffic sectors will be highly benefited. Those include Police, NGOs,
analysis it will result in exact responses to the user queries Statistical Bureaus and Universities to name a few. It will
and will lead to a greater amount of user satisfaction. provide a greater benefit to the society.
Keywords; AccSearch; road traffic; accident;Markov A. Literature Survey / Related Works
chain;accident prediction; Sergey Brin and Lawrence Page, “The Anatomy of Large-
Scale Hyper textual Web Search Engine” addressed the issue
I. INTRODUCTION
of developing a large scale search engine such as google but
Road accidents are the major problem in many countries. It failed to address the issue of specialized search[1]Sunny Lam,
is a very series problem in the highways of India. Internet is “The Overview of Web Search Engines,” addressed the issue
grown very large. As it is very large and the information is of how the search engines find information in the Web and
scattered all around the world, search engines are the only how they rank the pages according to the given query. It helps
medium through which the information can be accessed. But people perform Web searching easily and effectively. But it
the relevancy of the search result is the major problem in not address the issue of not getting the required information
search engines. Though popular search engines like Google even after search[2].
perform well through their quality of page ranking algorithms
still it is true that many questions remains unanswered up to Robert Steele, “Techniques for Specialized Search Engines”
their expected relevancy. Special purpose search engines are addresses the issue of the need for specialized search
those search engines which attempt to answer those questions engine.[3]Z Xiang, K. Wober, DR. Fesenmaier,
which are not answered or cannot be answered by an all “Representation of the Online Tourism Domain in Search
purpose search engines. Engines,” Addresses the issue of increasing the search results
in tourism domain using techniques.But failed to provide the
This project is an effort to create a comprehensive special lack of important information related to tourism on the web[4].
purpose search engine which will support with accurate Z Xiang, Bing Pan, K. Wober, DR. Fesenmaier, “Developing
responses with maximum possible relevancy till the very last SMART- Search : A Search Engine to Support the Long Tail in
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(IJCSIS) International Journal of Computer Science and Information Security,
Vol. 8, No. 2, 2010
Destination Marketing,” address the issue of effective after the model has been built[18].Pachaivannan Partheeban,
organizing over the internet to support travel information Elangovan Arunbabu, Ranganathan Rani Hemamalini, “Road
search. It didn’t address the issue of how to increase the Accident Cost Prediction Model Using Systems Dynamics
availability of travel information[5]. Approach,” addressed the issue of reducing the cost of accident
using developing model using systems dynamic approach. But
Karl W Wober, “Domain Specific Search Engines,” addresses not addresses that will it really lead to accurate cost
the techniques involved in domain specific search. But doesn’t prediction[19].
address the issue of how to implement the domain specific
search engine[6].YAN Hongfei, LI Jingjing, ZHU Jiaji, PENG
Bo, “Tianwang Search Engine at TREC 2005: Terabyte I. NEED FOR A SPECIALIZED SEARCH ENGINE
Track,” address the issue of large amount of data transfer. It
does not address the issue of improving the search results[7]. All purpose search engines are very broad and deemed to
Jermy Ginsberg, Mathew H. Mohebbi, Rajan S. Patel, cover almost all domains in the world. Though this quality is
Lynnette Brammer, Mark S. Smolinski & Larry Brilliant an advantage it includes some inabilities too. The main factors
“Detecting influenza epidemics using search engine query which influence any search engine and create the specialized
data,” address the issue of detecting influenza using the query need are found and listed as below:
data.It does not discuss about how avoid such epidemics using
that data[8].Gang Luo, Chunqiang, Hao Yang, Xing Wei, Specialization
“MedSearch : A Specialized Search Engine for Medical Availability of Information
Information,” addresses the issue of how to help layman in Responsibility
medical search but not addresses the issue of relevancy among Time elapsed
the medical information results[9].
A. Specialization
Jianhan Zhu, Jun Hong, and John G. Hughes, “Using Markov Though all purpose search engines support specialization of
Chains for Link Prediction in Adaptive Web Sites,” addresses information in response to the user queries, but they are
the navigation problems in adaptive web sites. But it does not mainly meant for generalization of information. Curious
address link prediction from the past state to future state[10]. search engines use the user queries which are unanswered or
Junghoo Cho, Hector Gracia Molina, Lawrence Page, not properly answered with expected relevancy to enhance
“Efficient Crawling through URL Ordering,” addresses the their system to answer well in feature. But at that point of time
issue of in what order the crawler should visit URLs. But not when user expects the right answer to his specialized queries
addressed the issue of taking care of the missed URLs which he won’t be able to get.
are not came in order of the crawler[11].
B. Availability of Information
Junghoo Cho, Hector Gracia Molina, “The evolution of the All purpose search engines gather information from all
Web and Implications of an Incremental Crawler,”addresses around the web. It has tons of information to serve the users. It
the issue of incrementally updating the index. But not will answer the maximum of the user queries. But it won’t be
addressed the issue of updating the indexes randomly able to answer all the queries. Because it doesn’t possess the
[12]Junghoo Cho, Hector Gracia Molina, “Parallel Crawlers,” information by its own. These search engines will struggle in
address the issue of managing the indexing of ever growing answering queries which requests in depth details within a
web. It doesn’t give the complete guidelines to construct particular domain.
parallel crawlers[13].Sanjay Kumar Singh, Ashish Misra,
“Road Accident Analysis : A Case Study of Patna City,” C. Responsibility
addresses the issue of Road accidents in Patna city. But not All purpose search engine tries through all the means to
addressed any road safety measures[14].G D Jacobs, Amy respond well for the user query and as well as update its
Aeron Thomas, “A Review of Global Road Accident information repository well to keep it fit for this activity. But it
Fatalities,” addresses the issue of deaths and injuries during bears no responsibility to answer the queries positively. Hence,
accidents. But not addressed how public and private sector can it is not sure for the user that his queries will be answered. It
act to prevent these injuries[15]. will be a trial and error process for him. All purpose search
engines works with probability not with accuracy in this
Ramasamy. N, “Accident Analysis of Chennai City,” aspect. Some search engine may handle some searches with
addressed the issue of accident analysis of Chennai city. But most probably high relevancy and for some other searches
not addressed how to avoid such accidents in future[16].Dinesh with less probability. This makes the user difficult to rely on
Mohan, “Social Cost of Road Traffic Crashes in India,” such kind of search engines.
addressed the issue of cost of injuries and deaths. But not
addresses how to eliminate those unwanted cost[17].P.Pramada D. Time elapsed
VALLI, “Road Accident Models for Large Metropolitan Cities Time elapsed in searching is the major factor which affect
of India,” addressed the issue of preventing accidents by road the interest of the user. When the time elapsed is more, it will
accident model. But not addresses how to avoid accidents even
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(IJCSIS) International Journal of Computer Science and Information Security,
Vol. 8, No. 2, 2010
create a greater amount of dissatisfaction in users. It has been VT5 – Two wheelers
found that the users are spending hours or sometime days in VT6 – Three wheelers
searching some essential information among the web. After VT7 – Others [bye cycle, bullock cart etc.,]
getting dissatisfied by their prolonged search they use to try
some other means to get that information i.e., making a series Type of accidents:
of phone calls, trying in yellow pages, physically going to the
concerned place to get that information etc. Hence, the efforts F1 – Fatal (Death)
made to reduce this time elapsed will bring a giant leap in the F2 – Grievous Injury
development of the advanced search engines. F3 – Minor Injury
F4 – Non Injury
The specialized search engine is aimed to address the above
factors which are not addressed by the all purpose search Time of accidents:
engines. Firstly it will concentrate on one domain and will have
sufficient collection of information to answer all sorts of Peak hours:
queries in that particular domain. As it is assured to answer all
the queries within that domain the user can fully rely on it. tp1 – 8:30 AM to 9:30 AM
Hence, it creates the full dependability to the user. The tp2 – 5 PM to 6:30 PM
specialized search engine AccSearch will contain all needed
information local to its domain. It will ensure the availability of Normal hours:
all the essential information. It bears the responsibility for the
information availability. It makes the user queries will be
tn1 – 10 AM to 5 PM
answered with full relevancy. It reduces the time elapsed in
searching by the user. It will answer the very first query itself tn2 – 7 PM to 8 AM (Cargo)
with full relevancy(whereas normally it needs many queries to B. The Process Flow of Accident Analysis
obtain an information in an all purpose search engine). At
maximum level he may need to try with very few queries.
Finally he can finish off his search in few minutes instead of Traffic
long time hassles. It has been found that there are regular users Authority
to search engine and they need to search for information for
their day to day activities. We identified the target users for
AccSearch. They are Police, NGOs, Statistical Bureaus,
Lawyers, Students to name a few. There will be bundle of
global users too. Once it attained perfection on its domain it Highways in
will be made to crawl the whole www so that it will work Chennai
specialized on its domain and generalized on all-purpose search
Vehicles on
II. MODIFIED MARKOV CHAIN ALGORITHM FOR ACCSEARCH the Road
A. Assumptions Four or Six Two or Three
wheelers wheelers
Types of vehicles : VT1, VT2, VT3, VT4, VT5, VT6, VT7 Select Type
of Vehicle
Types of accidents: F1, F2, F3,F4
Time of accidents: tn1, tn2, tp1,tp2
Other
Number of accidents : Ni
Vehicles
Search engine : S1, S0
Number of Number of Number of
Accidents in Accidents in Accidents in
Types of vehicles:
a year a year a year
VT1 – Government Bus
VT2 – Private Bus Classification Classification Classification
VT3 – Truck/Lorry of Accidents of Accidents of Accidents
VT4 – Car/Jeep/Taxi/Tempo
Accident Analysis
VT1 VT2 VT3 VT4 VT5 VT6 VT7
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(IJCSIS) International Journal of Computer Science and Information Security,
Vol. 8, No. 2, 2010
The equations which are the result of the accident analysis Else If
are given below. Each element in an equation represents the
percentage of the type of accident occurred with respect to the S1.Vehicle = VT6
total number of accidents by a particular vehicle type. &
S1.Time = tp1 & tp2
VT1 21.69F1 + 0.39F2 + 51.67F3 + 26.23F4
Type.Accident = S0.[7.92F1 + 0.93F2 + 80.18F3 + 10.95F4]
VT2 16.43F1 + 0.68F2 + 48.63F3 + 34.24F4
VT3 19.74F1 + 0.31F2 + 42.63F3 + 37.3F4 Else If
VT4 7.27F1 + 1.07F2 + 58.37F3 + 33.27F4
S1.Vehicle = VT7
VT5 11.31F1 + 1.38F2 + 81.14F3 + 6.15F4 &
S1.Time = tp1 & tp2
VT6 7.92F1 + 0.93F2 + 80.18F3 + 10.95F4
VT7 32.46F1 + 1.15F2 + 47.53F3 + 18.84F4 Type.Accident = S0.[32.46F1 +1.15F2 + 47.53F3 + 18.84F4]
As the vehicle types VT1 and VT2 are similar types (Bus)
End If
and the VT2 is available in only negligible amount and its
effect on the accidents is very low these two types can be
merged. III. MATHEMATICAL MODEL OF THE ALGORITHM
VT1 & VT2 19.6F1 + 0.535F2 + 50.15F3 + 30.235F4
The transition matrix has been constructed using these
available results.
C. Algorithm
Now the algorithm may be expressed as follows: V1 VT1 & VT2 19.6F1+0.535F2 + 50.15F3 + 30.235F4
V2 VT3 19.74F1 + 0.31F2 + 42.63F3 + 37.3F4
If
S1.Vehicle = VT1 & VT2 V3 VT4 7.27F1 + 1.07F2 + 58.37F3 + 33.27F4
&
S1.Time = tp1 & tp2 V4 VT5&VT6&VT717.23F1 +1.153F2+69.617F3+ 11.98F4
Type.Accident = S0.[19.6F1+0.535F2+ 50.15F3 +30.235F4] A. Transition Matrix
Else If V1 V2 V3 V4
S1.Vehicle = VT3 F1 .196 .1974 .0727 .1727
&
S1.Time = tn2 F2 .00535 .0031 .0107 .01153
T =
Type.Accident = S0.[19.74F1 + 0.31F2 + 42.63F3 + 37.3F4] .5015 .4263 .5837 .69617
F3
Else If .30235 .373 .3327 .1198
F4
S1.Vehicle = VT4
&
S1.Time = tp1 & tp2
Type.Accident = S0.[ 7.27F1 + 1.07F2 + 58.37F3 + 33.27F4] The above traffic prediction analysis expressed in terms of
transition matrix shows that the row represents fatality factors
Else If in correspondence with the vehicle classification.
S1.Vehicle = VT5 In the same manner the columns of the matrix shows that
& according to the vehicle types the percentage of fatality
S1.Time = tp1 & tp2 occurred. As this is a real time classification which have been
made in the Chennai city in the year 2008.
Type.Accident = S0.[ 11.31F1 + 1.38F2 + 81.14F3 + 6.15F4]
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IV. EXPERIMENTAL VALIDATION AND RESULTS
Predictions for V3
Predictions for V1 0.7
0.7
0.6
0.6
0.5
0.5
0.4
0.4
0.3
0.3
0.2
0.2
0.1
0.1
6E-16
F1 F2 F3 F4
6E-16
-0.1
F1 F2 F3 F4
-0.1 V3 (2010) V3 (2011) V3 (2012)
V1 (2010) V1 (2011) V1 (2012) V3 (2013) V3 (2008)
V1 (2013) V1 (2008)
Predictions for V4
Predictions for V2
0.7
0.7
0.6
0.6
0.5 0.5
0.4 0.4
0.3 0.3
0.2
0.2
0.1
0.1
6E-16
F1 F2 F3 F4 6E-16
-0.1 F1 F2 F3 F4
V2 (2010) V2 (2011) V2 (2012) -0.1
V2 (2013) V2 (2008) V4 (2010) V4 (2011) V4 (2012)
V4 (2013) V4 (2008)
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(IJCSIS) International Journal of Computer Science and Information Security,
Vol. 8, No. 2, 2010
that 2011-2013 are nearby values and 2008 and 2010 are
Predictions for V1 distinct values. The values of 2011-2013 are 26.58%, 27.18%,
and 27.09% respectively. The values of 2010 and 2008 are
Predictions of F1 of 2010 – 2013are less when compared to 30.25% and 11.98%.
2008 whereas 2008 shows a value of 19.6% but 2010-2013
are found as 9.07%, 10.93%, 11.49% and 11.52%
respectively. In predictions of F2 2008 shows a lower value
that is 0.53% whereas 2010-2013 shows higher value i.e, V. CONCLUSION AND FUTURE WORKS
0.99%,1%, 1.02%, and 1.03% respectively. In the predictions
of F3 2010, 2012 and 2013 shows the higher values and the This paper presents AccSearch, a specialized web search
remaining shows the lower value. The values for 2010-2013 engine for road accident information retrieval. It will aid the
are 60.38%, 60.37 and 60.51 respectively. Similarly for 2011 user group consisting of police, NGOs, statistical bureaus,
and 2008 the values are 58.61% and 50.15%. Predictions of F4 lawyers, students and others who may require road accident
states that the value of 2008 is somewhat raised and 2010- information for their day to day activities. AccSearch is
2013 are somewhat lowered. The values are 26.43%, 26.37%, designed to be a scalable search engine. The primary goal is to
27.11% and 27.24% respectively whereas the 2008 value is provide a very high relevancy in search results.
30.23%.
In future this search engine will be enhanced as a semantic
Predictions for V2 search engine by creating ontology for this domain.
Predictions of F1 of 2011-2013 are showing nearby values
where 11.53%, 11.45%, and 11.45% respectively whereas the
values of 2010 and 2008 are different those are, 13.47% and ACKNOWLEDGMENT
19.74% respectively. Predictions of F2 shows that the values of This paper kindly acknowledges the Traffic Police,
2010 -2013are almost similar, those are 0.99%, 1.01%, 1.02%, Chennai,Tamil Nadu,India with whose support was very vital
and 1.02%. But 2008 shows 0.31%. Predictions of F3 shows and acknowledges the institution where the idea was nurtured.
that 2010 , 2012 and 2013 shows almost similar values those
are 60.88%, 60.88% and 60.16%. The values of 2011and 2008
showing distinct such as 59.94% and 42.63%. Predictions of REFERENCES
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269 http://sites.google.com/site/ijcsis/
ISSN 1947-5500
(IJCSIS) International Journal of Computer Science and Information Security,
Vol. 8, No. 2, 2010
[9]. Gang Luo, Chunqiang, Hao Yang, Xing Wei,
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ANNEXURE:
1. Among the total number of accidents fatal accidents are low in
numbers and the non-fatal are high in numbers. A sample trend is
shown below:
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5.Number of injuries as per the victim
2005 2006 2007 2008 2009
MCRIDER 1741 1785 1907 1927 1743
PEDESTRAIN 1418 1335 1297 1373 1270 10. Number of fatal injuries based on the road:
MCRIDER 1741 1785 1907 1927 1743 ROAD 2005 2006 2007 2008 2009
CYCLIST 493 407 347 347 241 100FeetRoad 55 38 34 43 43
AUTOOCCUPANT 314 251 301 275 208 OMR Road 38 35 33 52 44
AUTODRIVER 204 178 180 183 150 ECR Road 47 51 30 28 40
STANDPER 172 147 185 170 129 AnnaSalai 35 42 32 38 33
CAROCCUPANT 91 83 104 146 107 ArcotRoad 23 17 16 26 16
CARDRIVER 102 83 97 121 120 200FeetRoad 14 24 15 16 15
LORRYDRIVER 24 15 19 16 15 ThiruvotriyurHighRoad 18 0 0 0 103
VANOCCUPANT 51 23 25 31 29 SPRoad 11 6 9 11 8
VANDRIVER 33 28 19 23 19 Tharamani Road 9 7 13 8 6
VelacherryMainRoad 8 14 14 18 11
6. Number of fatal injuries as per the age for fatal male: SNChettyStreet 10 12 4 11 8
AGE 2005 2006 2007 2008 2009 EnnoreExpressRoad 5 9 8 12 12
15 to 29 130 149 182 169 11 DurgabaiDeshmulkRoad 5 6 6 4 4
NewAvadiRoad 4 6 11 6 4
30 to 44 131 139 117 141 116
PoonamalleeHighRoad 0 9 3 2 4
45 to 59 127 147 107 132 127
ABOVE60 88 89 83 81 99
BELOW14 16 15 12 7 11
7. Number of non-fatal injuries as per the age of male:
AGE 2005 2006 2007 2008 2009
15 to 29 1387 1364 1440 1398 1217
30 to 44 1254 1180 1170 1241 1044
45 to 59 793 695 765 783 713
ABOVE60 258 255 282 287 272
BELOW14 219 196 213 193 184
8. Number of fatal injuries as per the age of female:
AGE 2005 2006 2007 2008 2009
45to59 32 33 18 25 127
ABOVE60 28 21 28 41 29
30to44 20 18 23 13 17
15to29 17 18 17 12 16
BELOW14 7 10 10 6 6
9. Number of non-fatal injuries as per the age of female:
AGE 2005 2006 2007 2008 2009
30to44 306 283 289 291 286
15to29 287 253 242 278 210
45to59 229 200 255 240 215
ABOVE60 128 129 130 165 137
BELOW14 101 101 86 96 102
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