PREDICTION OF ROAD ACCIDENT MODELLING FOR INDIAN NATIONAL HIGHWAYS

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International Journal of Civil Engineering and Technology (IJCIET)Volume 8, Issue 1, January 2017, pp.

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PREDICTION OF ROAD ACCID

FOR INDIAN

Research Scholar, School

Rajeev Gandhi Memorial college of Engineering and Technology,

Professor

Vasi Reddy

Professor, Department of Civil Engineering,

Jawaharlal Nehru Technolo

ABSTRACT

The objective of this research article is

variables of a section of four

models that explains the relationship between frequency of accident count and highway safety

variables. The Highway traverses mainly through a plain terrain of mostly agricultural areas.

The study is for newly constructing Four

(Chagalamarri) to 359.9(Kurnool)

accidents. The predictive ability using Multiple linear regression model is under two

categories: First for the 2 lane sections and second for 4 lane sections separately. The

validation tools were applied to examine the ability of models to predict accidents.

Key words: Accident Prediction Model,

Cite this Article B.Naga Kiran, Dr. N. Kumara Swamy and Dr. C. Sashidhar, Prediction o

Road Accident Modelling For Indian National Highways

Engineering and Technology, 8(1), 2017, pp.

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IJCIET/index.asp 789

International Journal of Civil Engineering and Technology (IJCIET) Volume 8, Issue 1, January 2017, pp. 789–802 Article ID: IJCIET_08_01_093

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Scopus Indexed

ICTION OF ROAD ACCIDENT MODELLING

INDIAN NATIONAL HIGHWAYS

B.Naga Kiran

Research Scholar, School of Civil Engineering,


Nandyal, Andhrapradesh, India

Dr. N. Kumara Swamy


Reddy Venkataadri Institute of Technology,

Guntur, Andhrapradesh, India

Dr. C. Sashidhar


Jawaharlal Nehru Technological University,

Anantapur, Andhra Pradesh, India

The objective of this research article is to identify the most critical safety influencing

variables of a section of four-lane National Highway-18(old)/40(New)

plains the relationship between frequency of accident count and highway safety

. The Highway traverses mainly through a plain terrain of mostly agricultural areas.

The study is for newly constructing Four-Lane road between chainage 224.000

(Kurnool) to identify all safety deficiencies responsible for road




iction Model, Multiple Linear Regression, Model Validation

B.Naga Kiran, Dr. N. Kumara Swamy and Dr. C. Sashidhar, Prediction o

Road Accident Modelling For Indian National Highways. International Journal of Civil

, 8(1), 2017, pp. 789–802.


[email protected]


ENT MODELLING

NATIONAL HIGHWAYS


to identify the most critical safety influencing

through statistical

plains the relationship between frequency of accident count and highway safety

. The Highway traverses mainly through a plain terrain of mostly agricultural areas.

Lane road between chainage 224.000

to identify all safety deficiencies responsible for road




Multiple Linear Regression, Model Validation.

B.Naga Kiran, Dr. N. Kumara Swamy and Dr. C. Sashidhar, Prediction of

International Journal of Civil

asp?JType=IJCIET&VType=8&IType=1

Prediction of Road Accident Modelling For Indian National Highways

http://www.iaeme.com/IJCIET/index.asp 790 [email protected]

1. INTRODUCTION

As per the official records 1,41,526 persons were died and 4,77,731 were injured in road traffic

accidents in India in 2014(NCRB 2015).Hence Road safety Management has emerged as a topic of

discussion for researchers all over the world. The traffic police are the source for giving information

relating to road traffic injuries in India, based on the cases reported them. The burden of road traffic

injuries has increased over the last 20 years in India. National Safety policies are to be promoted to

ensure improvements in traffic safety. As per the reports of MORTH and NCRB (National crime

Record Bureau, the data can be useful up to 20% only and the remaining 80% is unreliable

information which should not be used for analysis. The faults in the reports probably occur from a

wrong coding of the victims status which needs to be reviewed &revised.

The fatalities are more than doubled between 1991 and 2014 in big cities of India. In Rajastan,

Maharastra, Orissa and Tripura fatalities increased by 4 to 6 times and in Assam, Gujarat, Punjab and

Haryana 8-10 times increased in the same period. Much concentration need to be put to street and

highways designs which influence on vulnerable road user safety ,because present policies do not

appear to be giving needed effect. Number of deaths were increased between 1996 to 2014 in almost

all cities in India Survey reports suggested that in Agra and Ludhiana lower volumes vehicle velocities

can be higher at night because sufficient lighting system is not available and there is limited

checking’s of drivers under the influence of alcohol.

In India, national highways are only 15% of the total length but account for 33% of the fatalities.

The expressway are only to a length of 1000kms in 2014 in the country but a high death rate of 1.8 per

Km per year.68% of persons are getting killed on highways in India by the vulnerable road users.

Rear end crashes including with parked vehicles are high on all types of highways. Safety should

be enhanced by separating roads for slow and fast moving vehicles on the same roads. An independent

road safety Agency is to be established in India to set standards, to monitor and implements.

Multi disciplinary research centers are to be established to get more innovative results. Highway

designs must be adequate with safe facilities for slow traffic and separate paths are to be provided for

bicycle lanes and disabled pedestrians.

2. LITERATURE REVIEW

Earlier Research Model Parameters Conclusion

Shankar et al(1994) Negative binomial model Road geometric, weather and

seasonal effects

They concluded that rainfall

played or significant positive role

in accident occurrences

Persaud et al(2000) Generalized linear model Traffic flow, Road geometry Accidents per year increases with

ADT

Golob and Recker(2003) Linear and non linear

multivariate statistical analysis

Traffic flow, Weather and

lighting

Significant variables are original

traffic flow variables

Wong et al(2007) Poisson and Negative

binomial regression

Traffic flow, geometric

design, road environment

Degree of curve more significant

role in crash risk

Quddus et al(2010) Ordered Response model Traffic congestion, road

geometry

Traffic congestion did not affect

the severity of road crashes

Xiugang Lietal (2011) Generalized additive models Geometric elements Lane and shoulder widths

significant role in crash risk

Anitha Jacob et al(2013) Passion regression,

NB regression model Geometrics elements

Crashes effectively reduced by

widening roads.

B.Naga Kiran, Dr. N. Kumara Swamy and Dr. C. Sashidhar


3. STUDY AREA

Government of India through NHAI has taken up the development, maintenan

national highways under NHDP phase

where the intensity of traffic has increased significantly and to augment the capacity for safe and

efficient movement of traffic.

National highway number 18 starts from Chittoor

length of highway is located in Andhra Pradesh and it passes

Allagadda (247.7), Nandya l (282.2),

Kurnool district. Most of this national highway study segments falls in rural areas

85%).The study area for this project is newly constructing four lane road between chainage

Chagalamarri (224.000) to Kurnool

3.1. ALIGNMENT

The present project study stretch

(NH-18) is under during construction. Hence the alignment has contains number of sharp horizontal

and vertical curves. This stretch passes thr

mountainous terrain stretches from km 306.5 to km 322.0.This alignment runs bisecting the existing

irrigation tank at km 351.8 near Thandrapadu village for approximately 200m length on about 3.0

high embakment.

The alignment does not run in high embankment except on approaches to major bridges, where the

height of embankment is upto 5m.Generally the existing road is o

Figure 1 Google Road Map Chagalamarri To Kur

3.1. DATA COLLECTION

Accident data was collected from the National Highway Authority of India

data was collected through field studies. For the purpose of collecting road geometry data, the road

was divided into two type of segments i.e 2 lane segments and 4 lane segments. A five years

July to 2015 November) accident data was collected from the National Highway Authority of India

(NHAI) at Kurnool district. And also 135.5 km length road geometric characteristics was col

such as carriage way, shoulder width, number of curves…..etc.,

3.2. ROAD GEOMETRIC DATA

The total length of selected study area is 135.5 km co

lane is 63.5 km is divided into 16 segments called

km is divided into 14 segments called B1,B2,B3……B14. The

individual chainage, road geometric

Number of curves (NC), Number of



Government of India through NHAI has taken up the development, maintenan

national highways under NHDP phase-III programme of 4/6 lining of 10,000 km length of highways,


highway number 18 starts from Chittoor (NH-4 jn.) and ends at Kurnool

length of highway is located in Andhra Pradesh and it passes via Cuddapah (167.7),

(282.2), Panyam (303.0).The present project study

Kurnool district. Most of this national highway study segments falls in rural areas


Chagalamarri (224.000) to Kurnool (356.502).

have both 2 lane as well as 4 lane because this National Highway

18) is under during construction. Hence the alignment has contains number of sharp horizontal

and vertical curves. This stretch passes through plain terrain. The existing alignment in the rolling and


irrigation tank at km 351.8 near Thandrapadu village for approximately 200m length on about 3.0


height of embankment is upto 5m.Generally the existing road is on 0.5m to 2.5m high embankment.

Google Road Map Chagalamarri To Kurnool, A.P

Accident data was collected from the National Highway Authority of India (NHAI). Road geometry


segments i.e 2 lane segments and 4 lane segments. A five years


(NHAI) at Kurnool district. And also 135.5 km length road geometric characteristics was col

such as carriage way, shoulder width, number of curves…..etc.,

3.2. ROAD GEOMETRIC DATA

The total length of selected study area is 135.5 km consisting both 2 lane and 4 lane.

lane is 63.5 km is divided into 16 segments called A1,A2,A3……A16 and Total length of 4 lane is 72

km is divided into 14 segments called B1,B2,B3……B14. The lane details consisting of each

individual chainage, road geometric characteristics such as Carriageway (CW),

of bridge (NB),Number of minor culverts (NMC),


[email protected]

Government of India through NHAI has taken up the development, maintenance and management of

III programme of 4/6 lining of 10,000 km length of highways,


4 jn.) and ends at Kurnool (NH-7 jn.).Total

67.7), Maidukur (194.0),

present project study passes through only

Kurnool district. Most of this national highway study segments falls in rural areas (approximately


have both 2 lane as well as 4 lane because this National Highway

18) is under during construction. Hence the alignment has contains number of sharp horizontal

ough plain terrain. The existing alignment in the rolling and


irrigation tank at km 351.8 near Thandrapadu village for approximately 200m length on about 3.0m


n 0.5m to 2.5m high embankment.

A.P

(NHAI). Road geometry


segments i.e 2 lane segments and 4 lane segments. A five years (2011


(NHAI) at Kurnool district. And also 135.5 km length road geometric characteristics was collected

nsisting both 2 lane and 4 lane. Total length of 2

,A3……A16 and Total length of 4 lane is 72

lane details consisting of each

(CW), Shoulder width (SW),

(NMC), Number of canals



(NCS),Number of junctions(NJ),Number of minor roads (MR),Width at minor roads

(WMR),Roadmarkings(RM),Roadsigns(RS),Roadcondition(RC),Shouldertype(ST),Shoulder

condition(SC),Land use(LU).

Majority of these two lane and four lane study stretch was straight. The total number of curves in

entire two lane stretch are 46.And the total number of curves in only 4 lane segments are 62. The

carriageway for two lane segments varies 8.7m,8.5m,8.0m….etc., While in case of four lane

carriageway varies 16.0m,14.0m,14.2m……etc., The shoulder width varies in two lane segments is

0.9m,1.2m..etc., The shoulder width varies in four lane segments 1.4m,1.5m etc.,

4. MODL DEVELOPMENT

Multiple linear Regression: The Normal linear regression model having (Y) as Response variable and

X. The Normal MRM (Multiple Regression model) developed to understand the relationship b/w a set

of variables that shows in a data set.

Y=βO+β1X+u.... 1

Y=f(X) +U

Where X1, X2---= explanatory variables

β1 β2=estimated model coefficients

U=Random error term (Assumed to be distributed Normally with variance and mean zero)

βO =Regression Constant

f(X)=Population regression function

This shows that Y has to be distributed normally with mean population regression function and

variance. Even though the development of model and Interpretation is so simple: its use in Accident

Analysis is restricted due to the following drawbacks

• Some variables are not follow normal distribution

• Response variable(Y) cannot have a negative value and, Accidents are countable events.

Even with above drawbacks this Multiple Regression model has been Implemented (Or) tried in

this Research paper. The model can be Implemented (Or) tried whenever a set of data from bigger area

is used for Modelling because the presence of a huge no of small effects acting Independently and

additively can be assumed to follow normal distribution.(Central Limittheore)

5. VALIDATION OF MODEL

The validity of model is carried out as follows:

• By finding the Coefficient of Determination (R2).

• By the comparison of results of total number of accidents found by model with the data obtained from

NHAI.

Coefficient of Determination (R2):

• It is defined as a ratio of the explained variance to the total variance of the independent variable y. The

value ofR2 lies between 0 and 1, the closer it is to 1, The better is the model.

The following fig 2 shows Sixteen two lane segments road geometric characteristics on

Minitab(17.0)software contains C1 as a dependent variable(Number of accidents) and

C2,C3……..C15 as a independent variables. In two lane road geometric segments only fourteen

independent variabls was selected for regression input.



Figure 2 Accidents Alo

Figure 3 Two Lane Segment Output Summary (Analysis)



Accidents Along with Geometric Charcteristics on Two Lane Segments

Two Lane Segment Output Summary (Analysis)


[email protected]

ng with Geometric Charcteristics on Two Lane Segments



Figure 4 Two Lane Segment Output Summary (Coeeficents)

The above figures 3&4 indicates regression data output on Minitab 17.0 version. In this dat

refers to Response(Number of accidents NA ).and C2, C3 ,C4 ,C5, C6…….. are refers to

Predictors(CW,SW,NC,NB…… etc).

6. MULTIPLE REGRESSION

The following model was selected as the best fit model, satisfying both statistica

as practical considerations. Sensitivity studies were conducted to calibrate the model and also study

the effect of each variable on the urban road accidents

Figure 5 Normal Probability Plot

f Road Accident Modelling For Indian National Highways


Two Lane Segment Output Summary (Coeeficents)

indicates regression data output on Minitab 17.0 version. In this dat

Number of accidents NA ).and C2, C3 ,C4 ,C5, C6…….. are refers to

(CW,SW,NC,NB…… etc).

MULTIPLE REGRESSION MODEL (FOR TWO LANE SEGMEN

The following model was selected as the best fit model, satisfying both statistica


the effect of each variable on the urban road accidents.

Normal Probability Plot for Two Lane Segments


[email protected]

Two Lane Segment Output Summary (Coeeficents)

indicates regression data output on Minitab 17.0 version. In this data C1

Number of accidents NA ).and C2, C3 ,C4 ,C5, C6…….. are refers to

(FOR TWO LANE SEGMENTS)

The following model was selected as the best fit model, satisfying both statistical (R2 = 0.912) as well




Figure 6

Figure 7 Residuals Versus Order For Two Lane Segments

EQUATION:

Y =-84.8+5.7CW-20.63SW+2.94NC-

16.6NJ+13.7NMR+1.93WMR+2.4RM30.7RS+8.69RC+21.9ST+4.6SC.



Residuals Versus Fits For Two Lane Segments

Residuals Versus Order For Two Lane Segments

-19.5NB-5.5NMC+7.7NCS-



[email protected]




Table 1 Comparision

SL OBSERVED VALUES

1 14

2 5

3 1

4 2

5 2

6 8

7 32

8 25

9 22

10 0

11 17

12 7

13 7

14 54

15 15

16 8

The following fig 8 shows fourteen four


C2,C3……..C15 as a independent variables. In two lane road geometric segments only twelve


Figure 8 Accidents Along With Geometric Charcteristics On Four Lane Segments



Comparision of Observed and Predicted Number of Accidents

OBSERVED VALUES PREDICTD VALUES DIFFERENCE

11.601

0.19

0.72

6.67

0.92

2.75

5.3

16

26.2

26.5

22.6

3.3

10.66

45.87

14

2.6

wing fig 8 shows fourteen four lane segments road geometric characteristics on


a independent variables. In two lane road geometric segments only twelve


Accidents Along With Geometric Charcteristics On Four Lane Segments


[email protected]

f Accidents

DIFFERENCE

2.399

4.81

0.28

4.67*

1.08

5.25

26.7

9

24.2

26.5

5.6

3.7

3.66

8.13

1

2

lane segments road geometric characteristics on


a independent variables. In two lane road geometric segments only twelve

Accidents Along With Geometric Charcteristics On Four Lane Segments



Figure 9 Four Lane Segment Out Put Summary (

Figure 10 Four Lane Segment



Four Lane Segment Out Put Summary (Analysis)

Four Lane Segment Output Summary (Coefficents)


[email protected]

Output Summary (Coefficents)



Figure 11 Normal Probability Plot For Four Lane Segments

Figure 12



Normal Probability Plot For Four Lane Segments

12 Residuals versus Fits For Four Lane Segments


[email protected]

Normal Probability Plot For Four Lane Segments



Figure 13

EQUATION

Y=0+14.35CW-82.7SW+11.26NC27.3NB+3.44NMC25.5NMR+1.24WMR+56.9RM

21.2SC

Table 2 Comparision of Observed Predicted Accidents

SL NO OBSERVED VALUES

1 49

2 1

3 23

4 42

5 21

6 45

7 6

8 7

9 6

10 11

11 3

12 16

13 16

14 40



Figure 13 Residuals Versus Order For Four Lane Segments

82.7SW+11.26NC27.3NB+3.44NMC25.5NMR+1.24WMR+56.9RM-

Comparision of Observed Predicted Accidents

OBSERVED VALUES PREDICTED VALUES

49 46.8

0.2

23 18.8

42 43

21 17.5

45 42.6

9.4

6.2

6.3

11 13.5

2.8

16 19.2

16 12.3

40 39.5


[email protected]

-5.6RS-43.1RC-13.1ST-

ERROR

2.2

0.8

4.2

1

3.5

2.4

3.4

0.8

0.3

2.5

0.2

3.2

3.7

0.5



Figure 14 Comparision of Observed and Predicted Number of Accidents of Two Lane Segments.

Figure 15 Comparision of Observed and Predicted Number of Accidents of Four Lane Segments

7. DISCUSSIONS

1. The model developed can be used for the highways having conditions similar to the study and can help

to take right decision in the direction of accidents management i.e. to decide and implement remedial

measures in the field of traffic safety.

2. For safety diagnosis and specially, identification of dangerous zones in network by ranking the sites by

their accident rates, the model can be very helpful.

3. The model can be used for evaluation of the effectiveness of a safety measure by comparing the

accident rates of two compatible samples of sites before and after the implementation and to predict

accidents, their nature, causes and pattern.

4. Also, the effect of various parameters like carriage width, shoulder width, number of minor roads,

number of curves etc; on the road traffic accidents can be studied with the help of model.

0

10

20

30

40

50

60

A1

A2

A3

A5

A5

A6

A7

A8

A9

A1

0

A1

1

A1

2

A1

3

A1

4

A1

5

A1

6


0

10

20

30

40

50

60

B1 B2 B3 B4 B5 B6 B7 B8 B9 B10B11B12B13B14




5. More over, the results can act as a quick guideline for road network planning and the authorities

concerned with accident mitigation measures.

8. CONCLUSIONS

Accident prediction model (APM) is developed by using multiple regression analysis for NH-18 From

chainage 224.7(at Chagalamarri) To 359.9(at Kurnool) based on the factors influencing road accident.

For two lane segments the dependent variable used in the model is number of accidents(Y). The

independent variables used in the as Carriageway (CW),Shoulder width(SW),Number of curves(NC),

Number of bridges(NB),Number of minor culverts (NMC), Number of canals (NCS), Number of

junctions(NJ), Number of minor roads (MR),Width at minor roads (WMR), Road markings

(RM),Road signs (RS), Road condition (RC),Shoulder type(ST),Shoulder condition(SC), Land

use(LU).

The model developed from the above variables is

Y=-84.8+5.7CW-20.63SW+2.94NC-19.5NB-5.5NMC+7.7NCS-


The coefficient of determination (R2) obtained is 0.912.

1. For four lane segments the dependent variable used in the model is number of accidents(Y). The

independent variables used in the as Carriageway (CW),Shoulder width(SW),Number of curves(NC),

Number of bridges(NB),Number of minor culverts (NMC),Number of minor roads (MR),Width at

minor roads (WMR), Road markings (RM),Road signs (RS), Road condition (RC), Shoulder type(ST),

Shoulder condition (SC),Land use(LU).

The model developed from the above variables is

Y=0+14.35CW82.7SW+11.26NC27.3NB+3.44NMC25.5NMR+1.24WMR+56.9RM-5.6RS-43.1RC-13.1ST-

21.2SC.

The coefficient of determination (R2) obtained is 0.979.

1. It has been clearly demonstrated that regression analysis has been successfully applied to formulate a

prediction model for system testing defects. By using statistical approach such as regression analysis,

the research can justify the reasons and significance of metrics from requirement, design and coding

phase in predicting defects for system testing. Moreover, it is also explained that in order to have a

good model, the prediction must fall between a defined minimum and maximum range so that it is

feasible to incorporate and implement defect prediction as part of software development process,

particularly test process.

2. Accident data from NHAI suggestion that there is a lack of proper in design of road and education to

road way safety. These weaknesses can be minimized through comprehensive corrective measures.

Local community initiatives to improve the conditions are very sparse and it is also conducted that

much greater effort, desirably with the support from international agencies and specialized institutes is

needed in combating the problem. Importantly, such effort would require considerable resources

particularly trained local personal, safety specialization and researchers so as to buildup indigenous

capacity and attain sustainable safety program.

3. It is suggested to further refine the model reported in this study using more number of variables (eg:

Traffic volume , Spot speed…..etc) to get a more realistic picture in predicting or forecasting accidents,

though accidents occurrence is random phenomenon and therefore we can not exactly predict future

trends by using any model or theory, but it is a very handy tool in the hands of planners and decision

makes to take remedial measures in advance by studying future trends using such models, to take

mitigation measures to minimize the accident rate to certain extent and to take other safety measures.



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