International Journal of Civil Engineering and Technology (IJCIET)
Volume 10, Issue 04, April 2019, pp. 2305-2318. Article ID: IJCIET_10_04_240
Available online at http://www.iaeme.com/ijciet/issues.asp?JType=IJCIET&VType=10&IType=04
ISSN Print: 0976-6308 and ISSN Online: 0976-6316
© IAEME Publication
Scopus Indexed
“STUDY OF CAB DRIVER CHARACTERISTICS
AND ACCIDENT PRONENESS”
Diwakar Mishra*
PG Student, Department of Civil Engineering, Sam Higginbottom University of Agriculture,
Technology and Sciences, Allahabad, India
Dr. R. K. Pandey
Professor, Department of Civil Engineering, Sam Higginbottom University of Agriculture,
Technology and Sciences, Allahabad, India
Atul
Assistant Professor, Department of Civil Engineering, Sam Higginbottom University of
Agriculture, Technology and Sciences, Allahabad, India.
*Corresponding Author
ABSTRACT
As you would expect, one of the India’s largest and densely populated states, Utter
Pradesh’s city, Allahabad, is holy religious place, educational hub and historical but
it is also noisy, polluted, crowded and typically chaotic. The streets of the city are
congested and encroached by other activities. Bus services in particular have
deteriorated and their efficiency of service have been declining thus persons turn to
individual vehicle because of this the number of vehicle in the streets is increasing. This
result not only restricts the flow of traffic, but also puts in danger the road user's life.
The total no of accident as well as related fatality in the city is increasing over the year.
This paper attempts to analyze the road accidents in Allahabad using annual data
from the year 2012-2017.The remainder of the paper is organized as follows-section-1
provides an overview of road accident scenario in Allahabad, Utter Pradesh and India.
Section 2 deals with the cause of accident, type of accident and black-spot of Allahabad
District. In section 3 deals the questionnaire and analysis of survey. Finally sections 4
summarize the report with result and future scope of the study. The methodology
adopted includes collecting the secondary data from the concerned authority,
conducting physical surveys (primary data) and analyzing the data for accident
prediction model by logistic regression method. Regression analysis also allows us to
compare the effects of variables measured on different scales.
Accident black spots are usually defined as places of the road (relatively) high crash
potentials. Allahabad District has 98 identified black spots out of 98 black spots, 71 are
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Study of Cab Driver Characteristics and Accident Proneness
located within the city limits while trans –Ganga and trans –Yamuna areas account for
16 and 11 accident prone areas respectively.
Keywords: Allahabad, Accident, Road, Analysis, Black spot
Cite this Article: Diwakar Mishra, Dr. R. K. Pandey and Atul, Study of Cab Driver
Characteristics and Accident Proneness, International Journal of Civil Engineering and
Technology, 10(4), 2019, pp. 2305-2318.
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1. INTRODUCTION
The word of accident proneness was coined by psychological research workers in 1926. Since
then the concept of these word is that certain individuals always have more potential than others
to sustain accidents, even if they are exposed to similar risk-has been questioned but seldom
seriously challenged (Rodgers & Blanchard; 1993).Accident proneness is a concept that refers
to an enduring or stable personality characteristic that predisposes an individual toward having
accidents (Haddon,Suchman& Klein; 1964)
1.1. ROAD ACCIDENT
Road accident was defined as accident which took place on the road between two or more
objects, one of which must be any kind of a moving object. It is a human tragedy. According
to the Ministry of Road Transport and Highways, 1, 47, 913 persons were killed and another
4,70,975 were injured in 4,64,910 road crashes in India in 2017. This translates in to 1274
crashes and 406 deaths every day or 54 crashes and 17 deaths every hour. However, the number
of road accidents has declined in comparison to the year 2016.
Table 1.1 Accident Statistics of India and Uttar Pradesh
YEAR
ACCIDENTCASES IN
INDIA
DEATH
INJURED
ACCIDENTCASES IN
UTTAR PRADESH
DEATH
INJURED
2013
2014
2015
2016
2017
4,86,476
4,89,400
5,01,423
4,80,652
4,64,910
1,37,572
1,39,671
1,46,133
1,50,785
1,47,913
4,94,893
4,93,474
5,00,279
4,94,624
4,70,975
30,516
31,034
32,385
35,612
38,811
16,004
16,287
17,666
19,320
20,142
23,024
22,337
23,205
25,096
27,507
In the year 2017, there were 1164 accidents recorded in Allahabad District, out of which
328 were reported fatal. In 2017, there are 1164 cases recorded already with 357of them being
fatal. Road traffic accident affects all age group and all genders, however more than 83%
drivers are males and age lie between 15-34 year and more than 53% victims are injured. The
data available in the Superintendent of police traffic’s office, statistical unit is used as primary
source of data.
Table 1.2 Accident Statistics, Allahabad Traffic Police
YEAR
ACCIDENT
DEATH
INJURED
2012
2013
2014
2015
2016
2017
933
1123
1101
1034
1100
1164
405
480
481
465
488
477
573
813
782
679
758
780
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The analysis of road accident data 2017 reveals that about 3 accidents and 2 deaths take
place every day in Allahabad District. According to investigation report in Allahabad more
cases of fatal road accidents were reported on highway, Phaphamau-Nawabganj, NainiGhoorpur and Paniki-Tanki, Bamrauli roads have been termed as sensitive for fatal road
accidents. Violation of speed limit and rash driving are the main Cause of accidents.
1.2. CAUSES OF ROAD ACCIDENT
What might cause accidents has always been a debatable topic. Various factors have been
enlisted as causes for the same, like inefficient geometric design, poor quality and maintenance
of the pavement, environmental conditions such as rainy, windy and the environment of the
driver, which includes the traffic around him, vehicle characteristics, both static and dynamic.
• Human factor (70-80%):- Human factor described as that which that driver do or not
do at the time of accident. It includes various types of characteristic like distract driving,
speeding, drunk driving, reckless driving, carelessness, etc.
• Vehicle factor (10-15%):- vehicle factor refers mechanical fault of vehicle or design of
vehicle as like Tire blowouts, break failure, vehicle mass, etc.
• The road and its condition (5-10%):-The road and its condition include all aspect of
road design and maintenance, construction work as like defective geometric design,
badly located advertisement boards, etc.
• Environmental Factors-Traffic Weather (5-10%):- The environmental factor those
factors that attribute to the environment of the immediate vicinity of the operator, rain,
snow, wind storms, hail storms, fog, etc.
1.3. CAB DRIVER BEHAVIOUR
The job patterns of cab drivers are complex and they usually work 8-12 hours .They do not
work for longer periods and they have less stress because they do job autonomy and are not
dependent on owners. They have the freedom to see how long they will work continuously,
when they will leave work for lunch time and other activities. However, they always make sure
that they do not miss trips during office hours and peak hours, because at that time their trips
provide an opportunity to earn more. Cab drivers seemed to enjoy their professional life much
more than the other drivers. Four characteristics which make a good driver:• Communication skills:- A cab driver must have excellent customer service skills when
dealing with clients. Most passengers prefer a cab driver who has good communication
skills.
• Patient/ Stress management skills:-Patience is one of the most important qualities of a
driver should have. Activities of heavy traffic, road construction, parking, and other
drivers will take patience test on a daily basis. Aggression can lead to bad decisions and
eventually cause accidents.
• Technical skills:-A cab driver should be able to perform maintenance tasks that help
ensure the cab meets compliance and other safety standards. They should have basic
knowledge of how a cab operates and be able to perform repairs as necessary, such as
changing a tire.
• Knowledgeable:-The rules and regulations of the road vary from time to time, driver
should be aware of it. Do you know that each city has some own driving rules? For
example, if you live in Allahabad, then you should know which time no entry will be
found on which route.
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1.4. OBJECTIVE OF THE STUDY
•
•
•
•
To conduct an exclusive study for Indian drivers who are driving predominantly in a
mixed traffic condition.
To Identify specific factors that causes of accidents for a given class of drivers.
To analyze the statistical data of accident cases in Allahabad city.
To design an “Input-Output” model that can classify any given driver under
consideration as an expert or novice, and his proneness to operation under increased
risk factors.
2. METHODOLOGY AND OBSERVATIONS
To achieve the objectives methodology is to be done. Accident data is collected from Allahabad
traffic police stations, cab drivers and accident prone stretches on the area. Accident models
will be developed considering various factors. For this work study area is to be identified for
collecting the required data.
The investigations were conducted on Cab Drivers operating in the City of Allahabad. Cab
drivers were chosen to be the sample set because of their prolonged exposure to road conditions
and traffic. Also, the responses recorded were very uniform with near less discrepancy. The
present study is analytical in nature, accidents and fatalities, causes of accidents, type of
accident are investigated then calculated regression used to a simple statistical tool.
Following is the Questionnaire the Drivers were subjected to:-
I. Personal Information
•
•
•
What is your Marital Status?
you have any secondary income?
Vision Details.
II. Physiological Characteristics
•
•
•
•
•
Do you Consume Alcohol or Smoke Cigarettes?
How many hours are you comfortable driving in a day?
How many additional driving hours above comfortable driving hours are you willing to
drive?
How many hours do you continuously drive before taking a break?
Preferred working time?
III. Psychological Characteristics
•
•
•
•
1-10, how annoyed do you get in heavy traffic conditions?
How does the presence of Traffic Police affect you?
How does your mood affect the driving?
What is your reaction of a driver overtaking you?
IV. Driving characteristics
•
•
On a scale of 1-10, how would you rate your awareness of traffic rules?
On a scale of 1-10, how actively have you broken traffic rules?
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Diwakar Mishra, Dr. R. K. Pandey and Atul
•
•
What is the speed range that you prefer driving at?
On a scale of 1-10, rate yourself in following lane discipline and speed limit Driving
History?
V. Accident History
•
•
•
•
Where you involved in any accidents?
If yes, the reasons for the accident?
When did the accident occur?
What are the offences that you have been fined for?
2.1. ACCIDENT PRIDICTION MODEL
Accident prediction models have been extensively used in the domain of road infrastructure
for the estimation of the expected number of accidents on road segments and junctions (Hauer,
et.al.,1988). While designing the accident prediction model, we took into account the data
specific features, specifically eleven factors selected to construct the model. The data of this
type are modeled for use of logistic regression model. When there are only two possible values
in the response variable, then it is desirable to have a model that predicts the value as 0 or 1 or
as a probability score between 0 and 1. Linear regression is not ability. Because, if you can not
restrict the estimated Y values in using linear regression to model binary response variables,
the resulting model 0 and 1.
2.2. LOGISTIC REGRESSION
Logistic Regression was chosen as a method to analyze data because the end result was to adopt
one of the known values, or in this case a binary value of 1(Yes) or 0(No), the class label being
“Occurrence of Accidents”. The following model was generated in R. logistic regression is
used to describe the data and to explain relationships between a dependent binary variable and
one or more nominal, gradual, interval or ratio-level independent variables.
From the above questions,11 factors/triggers were isolated and the data values were
subjected through Logistic Regression. R Programming was used to perform logistic regression
on the collected data.
The 11 factors considered are as follows:
1. Vision Details
2. Intoxication
3. Driving Period
4. Comfortable Driving Hours per Day
5. Continuous Driving Hours Before Taking a Break
6. How Annoyed Do You Get In Heavy Traffic Conditions?
7. Does Mood Affect Your Driving?
8. Awareness of Traffic Rules
9. Actively Breaking Traffic Rules
10. Preferred Driving Speed and
11. Following Lane Discipline and Speed Limit.
Logistic Regression is applied to the above datasets. The class label is “Accident” which
contains two values “Yes” and “No” which can be converted into binary value of 1 and 0 using:
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accident<-ifelse(drive$accidents=="Yes",1,0)
To construct model
#logistic regression model
Mymodel<glm(accidents~detailed.report.form$Vision.Details.+detailed.report.form$into
xication+detailed.report.form$Preferred.working.time.+detailed.report.form$Comfortable.Dri
ving.Hours.per.Day+detailed.report.form$Continuous.Driving.Hours.Before.Taking.a.Break+
detailed.report.form$Preferred.Driving.Speed+detailed.report.form$How.An0yed.Do.You.Ge
t.In.Heavy.Traffic.Conditions.+detailed.report.form$Does.mood.effect.your.driving.+detailed
.report.form$Actively.braking.traffic.rules+detailed.report.form$Awareness.of.Traffic.Rules
+detailed.report.form$Following.Lane.Discipline.and.Speed.Limit,data=detailed.report.formf
amily )
summary(mymodel)
The standard equation of regression is as follows:
Y= β0 + β1X1 + β2X2 +….+βpXp + ε
Where, β0, β1 .… βp are constants known as Model Partial Regression Coefficients,
X1,X2,…. are variables and ε is the Random Disturbance. The advantage with using R
Programming is the combinations it automatically generates with their relevant confidence
levels on the output. The below code is run to generate a prediction based on the regressed
model. The input variables are entered as per the driver’s data and the output is run through the
below code.
Prediction: In logistic regression, you need to set type = response in order to compute the
prediction probabilities. This function is not needed in case of linear regression.
#Predictor Variable(X)
X<-modelLM
summary(X)
Prediction 1:
y1<- data.frame(Vision.Details.="Imperfectvision(Lens required)",intoxication="none of the
above",Preferred.working.time="8a.m-10a.m",Comfortable.Driving.Hours.per.Day=7,Contin
uous.Driving.Hours.Before.Taking.a.Break=3,Preferred.Driving.Speed="40-60kmph",How.A
n0yed.Do.You.Get.In.Heavy.Traffic.Conditions=10,Does.mood.effect.your.driving="Noeffec
t",Actively.braking.traffic.rules=8,Awareness.of.Traffic.Rules=10,Following.Lane.Discipline
.and.Speed.Limit=8)
predict(X,y1,interval = 'response')
predict(X,y1,interval = "confidence")
predict(X,y1,interval = "prediction")
2.3. Linear Regression
It is one of the most widely known modeling techniques. Linear regression is usually among
the first few topics which people pick while learning predictive modeling. In this technique,
the dependent variable is continuous, independent variable can be continuous or discrete, and
nature of regression line is linear. Linear Regression is the most basic type of regression and
commonly used for predictive analysis. The purpose of regression is to examine one things:
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Diwakar Mishra, Dr. R. K. Pandey and Atul
How do a set of predictor variables affect the outcome of the dependent variable? Is the
model using the predictors accounting for the variability in the changes in the dependent
variable?
These regression estimates are used to explain the relationship between one dependent
variable and one or more independent variables. The simplest form of the equation with one
dependent and one independent variable is defined by the formula y = c + b*x, where y =
estimated dependent score, c = constant, b = regression coefficient, and x = independent
variable. We performed Multiple Linear Regression using R-Programming taking dependent
variable as “Were you involved in any accidents?”
Independent variables were taken as:1. What is your marital status?(Married=1,Single&others=0)
2. Do you have any secondary income? (Yes=1,No=0)
3. Vision Details?(Perfect=1,Imperfect=0)
4. Do you smoke/Drink alcohol? (Yes=1,No=0)
5. Does your mood effect your driving? (Frustrated=1,No Effect=0)
6. What is your reaction if a driver overtakes you? (Yes=1,No=0)
7. On a scale of 1-10, rate yourself in following lane discipline and speed limit?
8. How many hours do you continuously drive before taking a break?
9. On a scale of 1-10, how would you rate your awareness of traffic rules?
10. On a scale of 1-10, how annoyed do you get in heavy traffic conditions?
11. How many overall hours are you comfortable driving in a day?
R- Programme input:mydata<-read.csv(file.choose(),header = 1)
str(mydata)
head(mydata)
pairs(mydata[1:11])
#multiple linear regression
results<-lm(accidents~
Do.you.+Vision.Details.+How.many.hours.do.you.continuously.drive.before.taking.a.break.+
How.many.overall.hours.are.you.comfortable.driving.in.a.day.+On.a.scale.of.1.10.how.an0ye
d.do.you.get.in.heavy.traffic.conditions.+How.does.your.mood.effect.your.driving.+What.is.y
our.reaction.if.a.driver.overtakes.you.+On.a.scale.of.1.10.how.would.you.rate.your.awareness
.of.traffic.rules.+On.a.scale.of.1.10..rate.yourself.in.following.lane.discipline.and.speed.limit.
+What.is.your.reaction.if.a.driver.overtakes.you.+What.is.your.marital.status.
+Do.you.have.any.secondary.income.,data=mydata)
results
summary(results)
3.RESULTS
Statistical survey has been conducted in Allahabad city between 220 drivers. The numbers of
drivers are less but a collected data is prepared based on the information given by them. Now
that we’ve collected our statistical survey results and have a data analysis plan, it’s time to dig
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in, start sorting, and analyze the data. The following observations were made from the above
questionnaire:-
Graph 3.1 (Vision Details)
Graph 3.2 (Intoxication)
Graph 3.3 (Comfortable Driving Hour)
Graph 3.4 (Additional Comfortable Driving Hour)
Graph 4.5 (Continuously Drive Hour)
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Graph 4.6 (Preferred Working Time)
Graph 4.7 (Annoyed by heavy traffic condition)
Graph 4.8(Affect due to presence of traffic police)
Graph 4.9 (Mood effect driving pattern
Graph 4.10 (Reaction for overtakes)
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Graph 4.11(Awareness of traffic rules)
Graph 4.12 (Actively broken traffic rules)
Graph 4.13 (Prefer driving speed range)
Graph 4.14 (Lane discipline and speed limit)
Graph 4.15 (Involved accident)
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Graph 4.16 (Accident causes)
Graph 4.17 (Accident time)
Graph 4.18 (Fined due to offences)
Graph 4.19 (Type of accident)
3.1. Logistic Regression Result
Figure 4.1 (R-studio, logistic regression output)
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The class label “Accidents” assumes values between 0 and 1. With the event being
“Occurrence of Accidents”, the fit values give a probability of the success of the event, for a
given class of driver and his input data.
The least probable value turns out to be 0.04 and the maximum probable value is
0.95.Which means, 0.04 is the probability of the occurrence of accident for a driver with the
best input values. 0.95 is the probability of the occurrence of accident for a driver with the
worst input values.
For example
Figure 4.2 (R-studio, Predictive value)
The intermediate values are divided into the following range modules for classification:1. P<0.15 = Least Accident Prone
2. 0.15<P<0.35 = Less Accident Prone
3. 0.35<P<0.70 = Moderately Accident Prone
4. 0.70<P<0.93 = Accident Prone
5. 0.93<P = Severely Accident Prone
Fit value of 0.571003 is indicative of the driver being Moderately Accident Prone.
3.2. Linear Regression Result
Figure 4.3.1 (R-studio, linear regression output)
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Diwakar Mishra, Dr. R. K. Pandey and Atul
•
•
The R2 value is 12.4%. This tells us that 12.4% of the variation in accident proneness,
is reduced by taking into account different parameters.
The Adjusted R2 value — denoted "R-sq(adj)" — is 7.6%. When considering different
multiple linear regression models for accident proneness, we could use this value to
help compare the models.
4. CONCLUSION
Among various reasons that cause accidents, our data showed that over speeding is the most
prominent factor 32.3 percent and second is drunken driving 18percent. Other most important
factor of traffic violation in Allahabad city is using cell phone while driving. In investigation
around11.2 percent of drivers were accept for using cell phone during driving. The study shows
maximum number of accidents (42.4percent) occurs in evening (6-9 p.m.) due to the heavy
vehicular movements. Second major accidents (26.5percent) occurring time was afternoon (36p.m.) in all the season.
Objective to design a model, which predicts the accident proneness of any driver under
testing, was achieved through R-Programming. Less experienced drivers, who get annoyed
with traffic conditions and smoke, are more likely to cause accidents than drivers who do not
get annoyed in traffic as the drivers who have been involved in accidents consistently show
higher annoyance levels compared to drivers who have not been involved in accidents. Drivers,
who drive continuously without long break, are often involved in more accidents than drivers
who often take a break. Even experienced and mature drivers who like to drive to drive at night,
they are actively involved in more accidents when breaking the rules.
Drivers, whose mood affects their driving pattern, tend to break rules more actively,
resulting in them being accident-prone. High speed, carelessness, lax laws, old vehicle, bad
roads, drunk driving, teenage driving, overloaded vehicle, lack of proper training for driving ,
ignorance of traffic rules , lack of hospital and trauma center are the main cause of mortality
due injuries on roads .
ACKNOWLEDGEMENT
The authors would like to acknowledge the support of Sam Higginbottom University of
Agriculture technology & Sciences, Allahabad for providing the resources used in conducting
this research work.
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