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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
B. AmuthaComputer Science and Engineering Department
SRM University
India
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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8/9/2019 AccSearch: A Specialized Search Engine for Traffic Analysis
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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
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,
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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.
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(IJCSIS) International Journal of Computer Science and Information Security,
Vol. 8, No. 2, 2010
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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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ISSN 1947-5500
8/9/2019 AccSearch: A Specialized Search Engine for Traffic Analysis
http://slidepdf.com/reader/full/accsearch-a-specialized-search-engine-for-traffic-analysis 8/8
(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
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ISSN 1947-5500