Road Accident Prediction for Nashik City - IJETMAS of Nashik traffic police, Sharanpur Road Nashik....

International Journal of Engineering Technology, Management and Applied Sciences

www.ijetmas.comMay 2017, Volume 5, Issue 5, ISSN 2349-4476

75 V.S. Aher, K.H. Ahirrao, M.R. Gaikar, S.S. Surve, R.R. Kshatriya

Road Accident Prediction for Nashik City

V.S. Aher1, K.H. Ahirrao

2, M.R. Gaikar

3, S.S. Surve

4, R.R. Kshatriya

5

1 to 4 UG Student, Department of Civil Engineering, Matoshri College of Engineering and Research Centre,

Nashik, Maharashtra, India. 5 Assistant Professor, Department of Civil Engineering, Matoshri College of Engineering and Research

Centre, Nashik, Maharashtra, India.

Abstract

In today‟s time due to galloping urbanization and development ridership has increased. This has subsequently

led to perilous situations for drivers as well as for pedestrians as the number of accidents over the past years

has significantly increased. The study focuses on developing a regression model to predict the number of

accidents along the selected stretch of road in Nashik city of India. Best fit trends for various parameters have

been found to arrive at the best model from the analysis using MS Excel 2007 and Origin Software.

Keywords: Road Accidents, Accident Prediction, Road Geometrics, Traffic Volume

1. Introduction

There has been rapid increase in urbanization which has led to better lifestyle of people. But these

advancements has created burden on roads due to increase in vehicle ownerships due to which problems

related to traffic have escalated to an alarming rate. The major issue with this increase is the increase in road

fatalities which causes individual losses and economical losses, Hence, there is a basic requirement for

assessing these accidents through identification of the various causes that are responsible for their occurrence,

analyze their dependency on those causes and also to recommend various remedial measures to alleviate

losses due to these accidents.

According to road accident statistics of 2011 by Ministry of Road and Highway Transport research wing, In

Maharashtra the number of accidents has increased by 12.7 percentages in the year of 2015 and the

Maharashtra state road transportation corporation (MSRTC) has identified 158 accident prone spots on major

road passing through the Pune region. According to the survey 2010 accident prone spot in the state of these,

the highest (392) are in the Mumbai Region followed by Nashik (286). Among the major Roads passing

through the Pune Region, The Pune Aurangabad Highway has been identified as having the highest numbers

of accident prone spot (180).

World Health Organization indicates that India has the highest number of traffic accident fatalities in the

world. Various factors such as increase in number of vehicles, faults in geometric design, attitude of drivers,

age of drivers, type of vehicle et cetera are responsible for increase in number of accidents. Therefore, to

assuage the damages caused by accidents it is important of predict the number of accidents before its

occurrence. Following statistics have been given by Transport Research Wing of Ministry of Road Transport

and Highways.

In 2013- 4,86,476 road accident deaths- highest in the world-25.2 per cent (1, 22,589) were fatal

accidents.

Major contributor to traffic deaths in India has been attributed to mistakes of drivers and also drunk

and drive, which is responsible for 70% of road fatalities.

India accounts for about 10 percentage of road accident fatalities worldwide. An estimated 12,75,000

persons are grievously injured on the road every year.




An accident is defined as as an event which comprises of damage to the property and/or injury to the road

users, which are recorded first by the police and/or emergency services. Prediction of accidents is generally

done by using statistical methods. India currently has only one mechanism of collecting road accident data and

that is from the police accident investigations. The police data is analyzed by two government organizations to

publish two annual reports - The National Crime Record Bureau‟s “Accidental Deaths and Suicides in India”,

and the Transport Research Wing, ‟Ministry of Road Transport and Highways‟ “Road Accidents in India”

report. These reports provide an understanding of fatal road traffic accidents at a national level and

information such as type of road users (age, sex, and vehicle type), incidence rates for states and cities, road

type, etc.

2. Study Area and route details

The present study is being done in Nashik city in the state of Maharashtra, India. Nashik is one of the busiest

cities of Maharashtra which has made it more prone to accidents. According to a survey, Nashik accounts for

highest number of traffic violation in Maharashtra. According to the survey 2010 accident prone spot in the

state of these, the Mumbai Region followed by Nashik (286), among the major Roads passing through the

Pune Region, The Pune Aurangabad Highway has been identified as having the highest numbers of accident

prone spot (180).

Image:1 Road Map Of Nashik City




In 2014, approximately 20,252 accidents were recorded in Nashik city. Also, in 2014 there has been an

increase of 20 percentages in number of fatal accidents. All the areas were studied and based on number of

accidents, availability of high speed road and feasibility of location, the national highway of Mumbai-Agra

passes via city Nashik. It is also well connected to city Pune with highway NH-50. Nashik is actually a main

road connection of main state highways. Nashik is also connected to Surat, Mumbai, and Aurangabad, Pune,

Dhule, Ahmednagar and all other important cities of India. The NH-3, national highway is being changed into

multi-lane highway and this multi-lane street has about six flyovers that will go through city of Nashik. The

flyovers will begin from main garvare point and will reach at temple of Hanuman at Panchvati and Nashik-

Pune highway NH-50. Apart from these, other major cities like Aurangabad (200 km) are connected via state

highway which is also four lane highway. Nashik is easily accessible by road from Gujarat state in Western

India, has been selected for present study. The entire stretch has been divided into sections, each being bound

between two consecutive major intersections so that homogeneity can be maintained and analysis can be made

easier. On an average 40% trips are made by two-wheelers, 43% trips are made by four-wheelers, 9 % by

three-wheelers and the rest trips comprises of those made by buses, trucks and LCVs. The map shows entire

stretch on Google Maps.

3.Methodology

The major activity in such research work was to identify databases of data collection, geometrical design,

traffic control, traffic volume and traffic accident data for at-grade (level) intersections outside urban areas

(rural intersections), that were suitable for testing the methodology and the development(re establishment) of

statistical models for traffic accident prediction.

The intent of this paper is not to replicate the detailed discussions of the methodological alternatives provided

in those papers, but instead to focus on discussing the methodological evolution, the current methodological

frontier and remaining methodological issues (the interested reader is referred to those papers for a review of

previously used methodological approaches).

4. Data Collection and Analysis

Vehicle Population:

The data related to the vehicle population are collected from the Nashik Regional Traffic Office (RTO) of the

Nashik. Mr. Pawar Sir, gives the data of vehicle population of the 2015, of the Total Nashik district vehicles

with category wise.

Accident Data:

All the required data which have been used to give a structure to this paper are being collected from the Head

office of Nashik traffic police, Sharanpur Road Nashik. It is the only source of Collection of the data related to

road traffic accident in this area. All accidental data collected from the police from the traffic department.

Generally there is a very high level of data availability (exceeding 90%) for accident types, accident severity

and additional outside accident influences, both for motorways / freeways and for two-lane two-way rural

roads. It is notable that the availability rate of prevailing accident cause data is somewhat lower.

The information recorded consists of numerous types of data, such as crash information which includes

number of accidents, accident severity and vehicle type, field information such as major/minor road width,

lane marking, control type, geometry and location, and miscellaneous information such as weather, time of

accident, and Average Annual Daily Traffic (AADT). Since most of the above information is recorded by

Police and later digitized in raw form, it is required to reduce the above information customizing it for specific

analysis.




Table:1 Accidents Cause Due To Location

Sr

No Location 2009 2010 2011 2012 2013 2014 2015 2016

1 Near School or College 116 233 190 230 176 94 87 64

2 Near or Inside a village 81 115 65 75 48 31 68 NA

3 Near a Factory Industrial Area 89 79 52 64 54 38 40 NA

4 Near a Religious Place 35 35 32 32 12 15 33 34

5 Near a recreation place/cinema 52 24 35 28 26 14 27 NA

6 In Bazar 104 95 71 89 70 47 74 95

7 Near Office complex 64 24 48 59 42 27 67 53

8 Near Hospital 33 33 42 26 25 9 33 27

9 Residential area 103 70 67 82 191 184 271 179

10 Open Area 155 85 87 195 320 312 362 453

11 Near Bus Stop 103 50 49 38 42 46 67 50

12 Near Petrol Pump 60 34 41 14 16 16 21 42

13 At Pedestrian Crossing 63 37 59 31 48 11 44 25

14 Affected by Encroachments 43 11 25 3 0 0 1 0

15 Narrow Bridge or Culverts 8 13 17 9 1 2 5 9

16 Other 0 40 72 48 56 27 101 NA

Total 1109 978 952 1023 1127 873 1301 1031

Graph:1 Area Wise Distribution of Accidents

0

200

400

600

800

1000

1200

1400

1600

1800

2000

5. ACCIDENTS CAUSE DUE TO LOCATION

No O

f A

ccid

ents




Table:2 Total Number of Accidents According to Year

Sr.

No

Year

No. of Accidents No. of Person

Fatal Grievous Inj. Minor

Inj.

Non

Inj. Total Killed Injured

1 2009 132 101 208 668 1109 137 361

2 2010 121 91 283 678 1173 135 468

3 2011 106 138 232 550 1026 114 448

4 2012 145 189 206 483 1023 155 471

5 2013 108 294 229 496 1127 115 616

6 2014 133 239 142 358 872 137 409

7 2015 225 309 181 586 1301 239 679

8 2016 203 266 159 403 1031 213 599

Total Number of Accidents According to Year

1109

1173

1026

1023

1127

872

1301

1031

0

200

400

600

800

1000

1200

1400

2009

2010

2011

2012

2013

2014

2015

2016

No Of Accidents According To Year

No Of

Accidents




Table :3 Nature Of Accidents

SR.NO. NATURE OF 2009 2010 2011 2012 2013 2014 2015 %

ACCIDENTS

1 Overturning 81 109 0 42 45 15 239 13.47

2 Head on collision 204 211 118 366 465 468 360 33.77

3 Rear & collision 137 140 0 45 123 97 57 6.42

4 Collision brush/side swipe 149 86 0 94 76 57 44 5.96

5 Right angled collision 89 134 2048 39 66 44 30 3.25

6 Skidding 98 83 28 51 43 28 39 2.55

7 Right turn collision 90 59 29 32 50 9 21 1.85

8 Hit & run 115 82 24 142 104 80 146 13.47

9 Others 74 75 118 56 155 57 365 16.27

10 Hit from Back 0 979 0 1022 0 0 0 1.74

11 Hit fixed objects 0 0 0 0 0 0 0 0.42

12 Hit pedestrian 0 0 0 0 0 0 0 0.83

TOTAL 1037 1958 2365 1889 1127 855 1301 100

Graph :3 Nature Of Accidents

10.54%

28.38%

9.15%8.60%

5.88%

5.23%

4.37%

13.49%

12.80%

0.90%

0.22%

0.43%

Nature Of Accidents

Overturning

Head on collision

Rear & collision

Collision brush/side swipe

Right angled collision

Skidding

Right turn collision

Hit & run

Others

Hit from Back

Hit fixed objects

Hit pedestrian




Table :4 Accidents Due To Age of Persons

Details of Drivers Number of Accidents

2009 2010 2011 2012 2013 2014 2015 2016 Total %

(A) Total Driver's Age

1. Less than 18 years 1 57

15 51 30 52 7 213 1.00

2 18-25 489 280

301 298 230 302 235 2135 10.04

3. 25-35 240 142

192 263 200 332 305 1674 7.87

4. 35-45 200 118

142 226 147 295 243 1371 6.44

5. 45-60 177 213

156 232 137 229 152 1296 6.09

6 .60 and Above 3 63

29 37 31 45 39 247 1.16

7. Unknown Age 0 15

4 21 6 45 50 141 0.66

Total 1110 888 793 839 1128 781 1300 1031 7870 37.00

(B) Total Educational

Quali. of Driver

1. Less than 8th Standard 295 227

228 120 203 17 1090 5.12

2. Standard 8-10 376 250

391 240 363 403 2023 9.51

3. Standard 10 and Above 428 352

509 403 733 605 3030 14.24

4. Qualification not known 0 0 0 0 0 0 0 6 6 0.03

Total 1099 829 361 847 1128 763 1299 1031 7357 34.58

(C)Total Type of Licence

0

1. Regular Licence 1085 589

88 688 1131 1031 4612 21.68

2. Learner's Licence 13 109

44 23 61 0 250 1.18

3. Without Licence 10 18

1128 8 20 0 1184 5.57

Total

6046 28.42

(A) +(B) +(C) TOTAL 1103 716 340 510 1260 761 1212 1031 21273

100.0

0

Graph :4 Accidents Due To Age of Persons

37%

35%

28%

Accidents Due To Age Of Person

(A) Total Driver's Age

(B) Total Educational

Quali. of Driver

(C)Total Type of Licence




Table: 5 Accidents Due To Time of Day

Time Year

Total % 2009 2010 2011 2012 2013 2014 2015 2016

06.00 to 9.00 hrs (Day) 17 122 89 102 114 243 109 108 904 8.48

09.00 to 12.00 hrs (Day) 73 225 154 258 219 466 208 157 1760 16.50

12.00 to 15.00 hrs (Day) 160 116 109 137 153 515 209 150 1549 14.52

15.00 to 18.00 hrs (Day) 139 131 138 147 191 607 174 190 1717 16.10

18.00 to 21.00 hrs (Night) 214 142 141 154 186 623 224 189 1873 17.56

21.00 to 24.00 hrs (Night) 132 96 136 100 143 475 161 151 1394 13.07

00.00 to 3.00 hrs (Night) 49 66 94 77 65 302 101 62 816 7.65

03.00 to 6.00 hrs (Night) 31 79 91 46 56 272 54 24 653 6.12

TOTAL 815 977 952 1021 1127 3503 1240 1031 10666 100

Graph: 5 Accidents Due To Time of Day

5. Conclusion

From the accident data 2005 To 2016, it is observed that out of total accidents that have occurred in

Nashik city 24.86% of vehicles involved were four-wheelers and 99% of vehicles involved were two-wheelers

and vehicles were commercial vehicle like buses (18.19%) and trucks& Lorries (11.36%) and 50% of

accidents due to commercial vehicles were fatal.

From Table 5, it is observed that during morning peak hours 9:00 hrs to 12:00 hrs (Day) maximum

number of accidents has occurred which indicates that this is because of under-performance of carriageway

and not due to high speed. This situation remains the same till 6 p.m. in the evening. This observation supports

the final expression obtained to predict number of accidents which shows that carriageway width is a major

parameter influencing number of accidents.

8.48

16.50

14.52

16.10

17.56

13.07

7.65

6.12

ACCIDENTS DUE TO TIME OF DAY

06.00 to 9.00 hrs (Day)

09.00 to 12.00 hrs (Day)

12.00 to 15.00 hrs (Day)

15.00 to 18.00 hrs (Day)

18.00 to 21.00 hrs (Night)

21.00 to 24.00 hrs (Night)

00.00 to 3.00 hrs (Night)

03.00 to 6.00 hrs (Night)




Also, it can be observed from Table 7 that after evening peak hours, i.e. after 18:00 hrs. To 21:00 hrs

(Night) accidents are more which can be attributed to high speeds of vehicles as the traffic volume reduces

after 21:00 hrs. which also supports final expression for predicting number of accidents.

References 1. Fatima Pereira Silva, Jorge Almeida Santos, Anderia Meireles, CongresoPanamericano de , Transporte, y

Logistica (panam 2014) „‟Road Accident: Driver Behaviour, Learning and Driving Task‟‟(adapted by

Carvalhasis,2002),Procedia Social and Behavioral Sciences 162, (2014), 300-309.

2. De Ona Juana, Garach Laurab, Ph.D. TRYSE Research Group. Department of Civil Engineering, University of

Granada, has published the paper „‟Accidents prediction model based on Speed Reductions on Spanish Two lanes

Rural Highways‟‟ Procedia Social and Behavioral Sciences 53 (2012), 1011-1019.

3. Punit Goyala , Sanskruti Joshi, Hemanth Kamplimath C, Dhruv Prajapati D, By „‟Accident Prediction

Modelling Of Major Road Of Ahmedabad‟‟ Abstract number 268, 12th

TPMDC IIT Bombay 19-21 December 2016

under graduate student and assistant professor of civil engineering Dept. Nirma University Ahmedabad, Gujrat,

India.

4. Kazimierz Jamroz, Wojciech Kustra, Marcin Budzynski a Gdansk University of Technology, 12 Narutowicza

strs, 80-232, Poland. 6th

Transport research Aerene April 18-21, 2016, „‟Pedestrian protecton, speed enforcement and

road network structure the key action for implementing poland‟s vision zero‟‟ in 18th

April 2016.

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