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Page 1: AccSearch: A Specialized Search Engine for Traffic Analysis

8/9/2019 AccSearch: A Specialized Search Engine for Traffic Analysis

http://slidepdf.com/reader/full/accsearch-a-specialized-search-engine-for-traffic-analysis 1/8

(IJCSIS) International Journal of Computer Science and Information Security,

Vol. 8, No. 2, 2010

AccSearch: A Specialized Search Engine for Traffic

Analysis

K. RenganathanComputer Science and Engineering Department

SRM University

India

[email protected]

B. AmuthaComputer Science and Engineering Department

SRM University

India

[email protected]

 Abstract— AccSearch is a specialized web search engine to

provide information about road accidents within Chennai,

India and assist the traffic authorities, police, NGOs,

lawyers, students and statistical bureaus. The people who

are in need of road accident information for various

reasons are very much struggling to collect the correct

information under a single search. Special purpose searchengines are designed to work on a particular domain

which fill the gap where an all purpose search engine

lacks. As the existing search engines cannot do the traffic

search alone well for several reasons, we have designed a

search algorithm using Markov chain, to provide the

search information in a faster manner. The mathematical

proof of our modified Markov chain algorithm shows that

the speed and efficiency seems to be better in comparison

with the existing search algorithms. As Markov chain can

be used for prediction purposes, our search engine

concentrates on one particular domain which is traffic

analysis it will result in exact responses to the user queries

and will lead to a greater amount of user satisfaction.

 Keywords; AccSearch; road traffic; accident;Markov

 chain;accident prediction;

I. INTRODUCTION

Road accidents are the major problem in many countries. It

is a very series problem in the highways of India. Internet is

grown very large. As it is very large and the information is

scattered all around the world, search engines are the only

medium through which the information can be accessed. But

the relevancy of the search result is the major problem in

search engines. Though popular search engines like Google

perform well through their quality of page ranking algorithms

still it is true that many questions remains unanswered up totheir expected relevancy. Special purpose search engines are

those search engines which attempt to answer those questions

which are not answered or cannot be answered by an all

purpose search engines.

This project is an effort to create a comprehensive special

purpose search engine which will support with accurate

responses with maximum possible relevancy till the very last

URL result for any queries pertaining to the road accident

details within the Chennai city. It is aimed to provide a high

dependency to the user. It covers the entire accident data

which occurred in the four National Highways around Chennai.

It will provide information about the accident occurred in the

day and night around the highways. This information is used

to do the historical collection of data. This traffic searchengine can be later connected to the all purpose search engines

to add up the searching power and efficiency.

Markov chain algorithm is used to improve the performance

and speed up. Markov chains are well known for the

performance tuning and prediction.

Adding the information with the available information on

the internet is the fruit of this work. By providing some more

information with the already available information some

sectors will be highly benefited. Those include Police, NGOs,

Statistical Bureaus and Universities to name a few. It will

provide a greater benefit to the society.

 A. Literature Survey / Related Works

Sergey Brin and Lawrence Page, “The Anatomy of Large-

Scale Hyper textual Web Search Engine” addressed the issue

of developing a large scale search engine such as google but 

 failed to address the issue of specialized search[1]Sunny Lam,

“The Overview of Web Search Engines,” addressed the issue

of how the search engines find information in the Web and

how they rank the pages according to the given query. It helps

people perform Web searching easily and effectively. But it

not address the issue of not getting the required information

even after search[2].

Robert Steele, “Techniques for Specialized Search Engines”

addresses the issue of the need for specialized search

engine.[3]Z Xiang, K. Wober, DR. Fesenmaier,

“  Representation of the Online Tourism Domain in Search

 Engines,” Addresses the issue of increasing the search results

in tourism domain using techniques.But failed to provide the

lack of important information related to tourism on the web[4].

Z Xiang, Bing Pan, K. Wober, DR. Fesenmaier, “ Developing

SMART- Search : A Search Engine to Support the Long Tail in

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Vol. 8, No. 2, 2010

  Destination Marketing,” address the issue of effective

organizing over the internet to support travel information

search. It didn’t address the issue of how to increase the

availability of travel information[5].

Karl W Wober, “ Domain Specific Search Engines,” addresses

the techniques involved in domain specific search. But doesn’t

address the issue of how to implement the domain specific

search engine[6].YAN Hongfei, LI Jingjing, ZHU Jiaji, PENGBo, “Tianwang Search Engine at TREC 2005: Terabyte

Track,” address the issue of large amount of data transfer. It

does not address the issue of improving the search results[7].

Jermy Ginsberg, Mathew H. Mohebbi, Rajan S. Patel,

Lynnette Brammer, Mark S. Smolinski & Larry Brilliant

“  Detecting influenza epidemics using search engine query

data,” address the issue of detecting influenza using the query

data.It does not discuss about how avoid such epidemics using

that data[8].Gang Luo, Chunqiang, Hao Yang, Xing Wei,

“  MedSearch : A Specialized Search Engine for Medical

 Information,” addresses the issue of how to help layman in

medical search but not addresses the issue of relevancy among

the medical information results[9].

Jianhan Zhu, Jun Hong, and John G. Hughes, “Using Markov

Chains for Link Prediction in Adaptive Web Sites,” addresses

the navigation problems in adaptive web sites. But it does not

address link prediction from the past state to future state[10].

Junghoo Cho, Hector Gracia Molina, Lawrence Page,

“  Efficient Crawling through URL Ordering,” addresses the

issue of in what order the crawler should visit URLs. But not

addressed the issue of taking care of the missed URLs which

are not came in order of the crawler[11].

Junghoo Cho, Hector Gracia Molina, “The evolution of the

Web and Implications of an Incremental Crawler ,”addressesthe issue of incrementally updating the index. But not

addressed the issue of updating the indexes randomly

[12]Junghoo Cho, Hector Gracia Molina, “Parallel Crawlers,”

address the issue of managing the indexing of ever growing

web. It doesn’t give the complete guidelines to construct

parallel crawlers[13].Sanjay Kumar Singh, Ashish Misra,

“  Road Accident Analysis : A Case Study of Patna City,”

addresses the issue of Road accidents in Patna city. But not

addressed any road safety measures[14].G D Jacobs, Amy

Aeron Thomas, “  A Review of Global Road Accident 

Fatalities,” addresses the issue of deaths and injuries during

accidents. But not addressed how public and private sector can

act to prevent these injuries[15].

Ramasamy. N, “  Accident Analysis of Chennai City,”addressed the issue of accident analysis of Chennai city. Butnot addressed how to avoid such accidents in future[16].DineshMohan, “Social Cost of Road Traffic Crashes in India,”addressed the issue of cost of injuries and deaths. But notaddresses how to eliminate those unwanted cost[17].P.PramadaVALLI, “ Road Accident Models for Large Metropolitan Citiesof India,” addressed the issue of preventing accidents by roadaccident model. But not addresses how to avoid accidents even

after the model has been built[18].Pachaivannan Partheeban,Elangovan Arunbabu, Ranganathan Rani Hemamalini, “ Road   Accident Cost Prediction Model Using Systems Dynamics Approach,” addressed the issue of reducing the cost of accidentusing developing model using systems dynamic approach. Butnot addresses that will it really lead to accurate costprediction[19].

I. NEED FOR A SPECIALIZED SEARCH ENGINE

All purpose search engines are very broad and deemed to

cover almost all domains in the world. Though this quality is

an advantage it includes some inabilities too. The main factors

which influence any search engine and create the specialized

need are found and listed as below:

Specialization

Availability of Information

Responsibility

Time elapsed

 A. Specialization

Though all purpose search engines support specialization of 

information in response to the user queries, but they are

mainly meant for generalization of information. Curious

search engines use the user queries which are unanswered or

not properly answered with expected relevancy to enhance

their system to answer well in feature. But at that point of time

when user expects the right answer to his specialized queries

he won’t be able to get.

 B. Availability of Information

All purpose search engines gather information from all

around the web. It has tons of information to serve the users. Itwill answer the maximum of the user queries. But it won’t be

able to answer all the queries. Because it doesn’t possess the

information by its own. These search engines will struggle in

answering queries which requests in depth details within a

particular domain.

C. Responsibility

All purpose search engine tries through all the means to

respond well for the user query and as well as update its

information repository well to keep it fit for this activity. But it

bears no responsibility to answer the queries positively. Hence,

it is not sure for the user that his queries will be answered. It

will be a trial and error process for him. All purpose search

engines works with probability not with accuracy in thisaspect. Some search engine may handle some searches with

most probably high relevancy and for some other searches

with less probability. This makes the user difficult to rely on

such kind of search engines.

 D. Time elapsed 

Time elapsed in searching is the major factor which affect

the interest of the user. When the time elapsed is more, it will

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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

found that the users are spending hours or sometime days in

searching some essential information among the web. After

getting dissatisfied by their prolonged search they use to try

some other means to get that information i.e., making a series

of phone calls, trying in yellow pages, physically going to the

concerned place to get that information etc. Hence, the efforts

made to reduce this time elapsed will bring a giant leap in the

development of the advanced search engines.

The specialized search engine is aimed to address the abovefactors which are not addressed by the all purpose searchengines. Firstly it will concentrate on one domain and will havesufficient collection of information to answer all sorts of queries in that particular domain. As it is assured to answer allthe queries within that domain the user can fully rely on it.Hence, it creates the full dependability to the user. Thespecialized search engine AccSearch will contain all neededinformation local to its domain. It will ensure the availability of all the essential information. It bears the responsibility for theinformation availability. It makes the user queries will be

answered with full relevancy. It reduces the time elapsed insearching by the user. It will answer the very first query itself with full relevancy(whereas normally it needs many queries toobtain an information in an all purpose search engine). Atmaximum level he may need to try with very few queries.Finally he can finish off his search in few minutes instead of long time hassles. It has been found that there are regular usersto search engine and they need to search for information fortheir day to day activities. We identified the target users forAccSearch. 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 itwill be made to crawl the whole www so that it will work specialized on its domain and generalized on all-purpose search

II. MODIFIED MARKOV CHAIN ALGORITHM FOR ACCSEARCH

 A. Assumptions

Types of vehicles : VT1, VT2, VT3, VT4, VT5, VT6, VT7

Types of accidents: F1, F2, F3,F4

Time of accidents: tn1, tn2, tp1,tp2

Number of accidents : Ni

Search engine : S1, S0

Types of vehicles:

VT1 – Government Bus

VT2 – Private Bus

VT3 – Truck/Lorry

VT4 – Car/Jeep/Taxi/Tempo

VT5 – Two wheelers

VT6 – Three wheelers

VT7 – Others [bye cycle, bullock cart etc.,]

Type of accidents:

F1 – Fatal (Death)

F2 – Grievous Injury

F3 – Minor Injury

F4 – Non Injury

Time of accidents:

Peak hours:

tp1 – 8:30 AM to 9:30 AM

tp2 – 5 PM to 6:30 PM

Normal hours:

tn1 – 10 AM to 5 PM

tn2 – 7 PM to 8 AM (Cargo)

 B. The Process Flow of Accident Analysis

Classification

of Accidents

Classification

of Accidents

Classification

of Accidents

Accident Analysis

VT1 VT2 VT3 VT4 VT5 VT6 VT7

Traffic

Authority

Highways in

Chennai

Vehicles on

the Road

Select Type

of Vehicle

Number of 

Accidents in

a year

Number of 

Accidents in

a year

Number of 

Accidents in

a year

Four or Six

wheelers

Two or Three

wheelers

Other

Vehicles

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The equations which are the result of the accident analysis

are given below. Each element in an equation represents the

percentage of the type of accident occurred with respect to the

total number of accidents by a particular vehicle type.

VT1 21.69F1 + 0.39F2 + 51.67F3 + 26.23F4

VT2 16.43F1 + 0.68F2 + 48.63F3 + 34.24F4

VT3 19.74F1 + 0.31F2 + 42.63F3 + 37.3F4

VT4 7.27F1 + 1.07F2 + 58.37F3 + 33.27F4

VT5 11.31F1 + 1.38F2 + 81.14F3 + 6.15F4

VT6 7.92F1 + 0.93F2 + 80.18F3 + 10.95F4

VT7 32.46F1 + 1.15F2 + 47.53F3 + 18.84F4

As the vehicle types VT1 and VT2 are similar types (Bus)

and the VT2 is available in only negligible amount and its

effect on the accidents is very low these two types can be

merged.

VT1 & VT2 19.6F1 + 0.535F2 + 50.15F3 + 30.235F4

C. Algorithm

Now the algorithm may be expressed as follows:

If 

S1.Vehicle = VT1 & VT2

&

S1.Time = tp1 & tp2

 

Type.Accident = S0.[19.6F1+0.535F2+ 50.15F3 +30.235F4]

Else If  

S1.Vehicle = VT3

&

S1.Time = tn2

 

Type.Accident = S0.[19.74F1 + 0.31F2 + 42.63F3 + 37.3F4]

 

Else If 

 

S1.Vehicle = VT4

&

S1.Time = tp1 & tp2

  Type.Accident = S0.[ 7.27F1 + 1.07F2 + 58.37F3 + 33.27F4]

Else If 

 

S1.Vehicle = VT5

&

S1.Time = tp1 & tp2

 

Type.Accident = S0.[ 11.31F1 + 1.38F2 + 81.14F3 + 6.15F4]

Else If 

 

S1.Vehicle = VT6

&

S1.Time = tp1 & tp2

 

Type.Accident = S0.[7.92F1 + 0.93F2 + 80.18F3 + 10.95F4]

Else If  

S1.Vehicle = VT7

&

S1.Time = tp1 & tp2

 

Type.Accident = S0.[32.46F1 +1.15F2 + 47.53F3 + 18.84F4]

End If 

III. MATHEMATICAL MODEL OF THE ALGORITHM

The transition matrix has been constructed using theseavailable results.

V1 VT1 & VT2 19.6F1+0.535F2 + 50.15F3 + 30.235F4

V2 VT3 19.74F1 + 0.31F2 + 42.63F3 + 37.3F4

V3 VT4 7.27F1 + 1.07F2 + 58.37F3 + 33.27F4

V4VT5&VT6&VT717.23F1 +1.153F2+69.617F3+ 11.98F4

 A. Transition Matrix

The above traffic prediction analysis expressed in terms of 

transition matrix shows that the row represents fatality factors

in correspondence with the vehicle classification.

In the same manner the columns of the matrix shows that

according to the vehicle types the percentage of fatality

occurred. As this is a real time classification which have been

made in the Chennai city in the year 2008.

.196 .1974 .0727 .1727

.00535 .0031 .0107 .01153

.5015 .4263 .5837 .69617

.30235 .373 .3327 .1198

F1

F2

F3

F4

T =

V1 V2 V3 V4

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IV.EXPERIMENTAL VALIDATION AND RESULTS

-0.1

6E-16

0.1

0.2

0.3

0.4

0.5

0.6

0.7

F1 F2 F3 F4

Predictions for V1

V1 (2010) V1 (2011) V1 (2012)

V1 (2013) V1 (2008)

-0.1

6E-16

0.1

0.2

0.3

0.4

0.5

0.6

0.7

F1 F2 F3 F4

Predictions for V2

V2 (2010) V2 (2011) V2 (2012)

V2 (2013) V2 (2008)

-0.1

6E-16

0.1

0.2

0.3

0.4

0.5

0.6

0.7

F1 F2 F3 F4

Predictions for V3

V3 (2010) V3 (2011) V3 (2012)

V3 (2013) V3 (2008)

-0.1

6E-16

0.1

0.2

0.3

0.4

0.5

0.6

0.7

F1 F2 F3 F4

Predictions for V4

V4 (2010) V4 (2011) V4 (2012)

V4 (2013) V4 (2008)

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Predictions for V1

Predictions of F1 of 2010 – 2013are less when compared to

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,

0.99%,1%, 1.02%, and 1.03% respectively. In the predictionsof F3 2010, 2012 and 2013 shows the higher values and the

remaining shows the lower value. The values for 2010-2013

are 60.38%, 60.37 and 60.51 respectively. Similarly for 2011

and 2008 the values are 58.61% and 50.15%. Predictions of F4

states that the value of 2008 is somewhat raised and 2010-

2013 are somewhat lowered. The values are 26.43%, 26.37%,

27.11% and 27.24% respectively whereas the 2008 value is

30.23%.

Predictions for V2

Predictions of F1 of 2011-2013 are showing nearby values

where 11.53%, 11.45%, and 11.45% respectively whereas thevalues of 2010 and 2008 are different those are, 13.47% and

19.74% respectively. Predictions of F2 shows that the values of 

2010 -2013are almost similar, those are 0.99%, 1.01%, 1.02%,

and 1.02%. But 2008 shows 0.31%. Predictions of F3 shows

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 

F4 shows that 2010-2013shows almost similar values those are

24.74%, 27.66%, 26.9%, and 27.1%.

Predictions for V3

Predictions of F1states that 2010-2013 have almost similarvalues that is 11.63%, 11.44%, 11.5%, and 11.46%

respectively whereas 2008 represents 7.27%. Predictions of F2

is showing that 2011-2013 are same and 2010 is almost same

that is 1.02 for 2011-2013 , 1.05 for 2010 and 1.07 for 2013.

Predictions of F3 states that values of 2010-2013 are almost

same those are, 61.33%, 60.18%, 60.25%, and 60.16%. For

2008 it is 58.37%. Predictions of F4 states that the values of 

2010-2013 are almost same those are 26%, 27.43%, 27.07%

and 27.09% respectively. But the value of 2008 bears 33.27%.

Predictions of V4

  Predictions of F1 states that the values of 2011-2013 arealmost similar values say 11.75%, 11.39%, and 11.49%. The

year 2010 which is 10.74%. 2008 shows a value 17.27%.

Predictions of F2 states that the values of 2012 and 2013 are

similar and 2011 is almost similar, 1.02%, 1.02% and 1.03%.

2010 shows 9.8% and 2008 shows 1.153%. Predictions of F 3

states that 2011-2013 and 2008 have almost similar values

these have 60.79%, 59.99%, 60.25%, and 69.617% whereas

2010 shows a lower value 58.13%. Predictions of F4 shows

that 2011-2013 are nearby values and 2008 and 2010 are

distinct values. The values of 2011-2013 are 26.58%, 27.18%,

and 27.09% respectively. The values of 2010 and 2008 are

30.25% and 11.98%.

V. CONCLUSION AND FUTURE WORKS

This paper presents AccSearch, a specialized web search

engine for road accident information retrieval. It will aid the

user group consisting of police, NGOs, statistical bureaus,

lawyers, students and others who may require road accident

information for their day to day activities. AccSearch is

designed to be a scalable search engine. The primary goal is to

provide a very high relevancy in search results.

In future this search engine will be enhanced as a semantic

search engine by creating ontology for this domain.

ACKNOWLEDGMENT

This paper kindly acknowledges the Traffic Police,

Chennai,Tamil Nadu,India with whose support was very vital

and acknowledges the institution where the idea was nurtured.

REFERENCES

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[2]. Sunny Lam, “The Overview of Web Search Engines,”

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[5]. Z Xiang, Bing Pan, K. Wober, DR. Fesenmaier,

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[6]. Karl W Wober, “  Domain Specific Search Engines,”

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Lynnette Brammer, Mark S. Smolinski & Larry Brilliant

“  Detecting influenza epidemics using search engine

query data,” Nature, vol. 457, 19 February 2009.

269 http://sites.google.com/site/ijcsis/

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Vol. 8, No. 2, 2010

[9]. Gang Luo, Chunqiang, Hao Yang, Xing Wei,

“ MedSearch : A Specialized Search Engine for Medical

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[10]. Jianhan Zhu, Jun Hong, and John G. Hughes, “Using

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[16]. G D Jacobs, Amy Aeron Thomas, “  A Review of 

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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:

Fatal is shown as (2005: 584) (2006 : 627) (2007 : 583)

(2008:612)(2009:609)

Non fatal is shown as (2005:4427) (2006 :4657) (2007: 4277)

(2008:5774)(2009:4575)

Where the total number of accidents of these categories are: 5177

2. Among the various vehicle types two wheelers is the more prone to

accidents of fatal category. Lorrys are of the second categories

slightly less prone to accidents and so on.

2005 2006 2007 2008 200

Two wheeler 154 174 159 191 191

Lorry 108 120 113 104 103

MTC Bus 71 74 66 79 74

Car 60 72 75 75 77

Van 63 57 59 52 38

Auto 48 48 40 41 36

UKV 35 37 36 46 40

Others 20 12 5 4 12

Private bus 14 22 15 22 1

Govt. bus 10 7 5 15

3. Similarly vehicle types are classified based on non fatal injuries

on accidents

2005 2006 2007 2008 2009

Two wheeler 1501 1370 1373 1517 1438

Car 903 966 1065 1694 1229

Auto 720 695 669 664 493

Lorry 473 425 375 654 456

Van 433 384 359 590 442

MTC Bus 184 202 274 334 285

Private Bus 63 59 61 129 68

Others 59 55 21 59 50

UKV 44 47 40 46 62

Jeep 29 23 22 26 29

Govt. bus 18 16 18 38 23

4.Number of deaths as per the victim and as per the death:

2005 2006 2007 2008 2009

PEDESTRAIN 220 247 222 231 242

MCRIDER 148 200 211 236 202

CYCLIST 74 65 51 48 50

MCPRIDER 45 35 41 36 43

OTHERS 16 11 12 7 14

AUTODRIVER 11 6 9 7 4AUTOOCCUPANT 9 17 13 7 7

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(IJCSIS) International Journal of Computer Science and Information Security,

Vol. 8, No. 2, 20105.Number of injuries as per the victim

2005 2006 2007 2008 2009

MCRIDER 1741 1785 1907 1927 1743

PEDESTRAIN 1418 1335 1297 1373 1270

MCRIDER 1741 1785 1907 1927 1743

CYCLIST 493 407 347 347 241

AUTOOCCUPANT 314 251 301 275 208

AUTODRIVER 204 178 180 183 150

STANDPER 172 147 185 170 129

CAROCCUPANT 91 83 104 146 107CARDRIVER 102 83 97 121 120

LORRYDRIVER 24 15 19 16 15

VANOCCUPANT 51 23 25 31 29

VANDRIVER 33 28 19 23 19

6. Number of fatal injuries as per the age for fatal male:

AGE 2005 2006 2007 2008 2009

15 to 29 130 149 182 169 11

30 to 44 131 139 117 141 116

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 2930to44 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

10. Number of fatal injuries based on the road:

ROAD 2005 2006 2007 2008

100FeetRoad 55 38 34 43

OMR Road 38 35 33 52

ECR Road 47 51 30 28

AnnaSalai 35 42 32 38

ArcotRoad 23 17 16 26

200FeetRoad 14 24 15 16

ThiruvotriyurHighRoad 18 0 0 0

SPRoad 11 6 9 11

Tharamani Road 9 7 13 8

VelacherryMainRoad 8 14 14 18

SNChettyStreet 10 12 4 11

EnnoreExpressRoad 5 9 8 12

DurgabaiDeshmulkRoad 5 6 6 4

NewAvadiRoad 4 6 11 6

PoonamalleeHighRoad 0 9 3 2

271 http://sites.google.com/site/ijcsis/

ISSN 1947-5500