International Journal of Civil Engineering and Technology (IJCIET) Volume 10, Issue 04, April 2019, pp. 82-95. Article ID: IJCIET_10_04_010 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 http://www.iaeme.com/IJCIET/index.asp 82 editor@iaeme.com Diwakar Mishra, Dr. R. K. Pandey and Atul 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. 82-95. http://www.iaeme.com/IJCIET/issues.asp?JType=IJCIET&VType=10&IType=04 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 http://www.iaeme.com/IJCIET/index.asp 83 editor@iaeme.com Study of Cab Driver Characteristics and Accident Proneness 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. http://www.iaeme.com/IJCIET/index.asp 84 editor@iaeme.com Diwakar Mishra, Dr. R. K. Pandey and Atul 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? http://www.iaeme.com/IJCIET/index.asp 85 editor@iaeme.com Study of Cab Driver Characteristics and Accident Proneness • • 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: http://www.iaeme.com/IJCIET/index.asp 86 editor@iaeme.com Diwakar Mishra, Dr. R. K. Pandey and Atul 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: http://www.iaeme.com/IJCIET/index.asp 87 editor@iaeme.com Study of Cab Driver Characteristics and Accident Proneness 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 http://www.iaeme.com/IJCIET/index.asp 88 editor@iaeme.com Diwakar Mishra, Dr. R. K. Pandey and Atul 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) http://www.iaeme.com/IJCIET/index.asp 89 editor@iaeme.com Study of Cab Driver Characteristics and Accident Proneness 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) http://www.iaeme.com/IJCIET/index.asp 90 editor@iaeme.com Diwakar Mishra, Dr. R. K. Pandey and Atul 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) http://www.iaeme.com/IJCIET/index.asp 91 editor@iaeme.com Study of Cab Driver Characteristics and Accident Proneness 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) http://www.iaeme.com/IJCIET/index.asp 92 editor@iaeme.com Diwakar Mishra, Dr. R. K. Pandey and Atul 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) http://www.iaeme.com/IJCIET/index.asp 93 editor@iaeme.com Study of Cab Driver Characteristics and Accident Proneness • • 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. REFRENCES [1] [2] [3] [4] [5] Cardamone A., Eboli L., Forciniti C., and Mazzulla G., (2015), “How usual behaviour can affect perceived drivers psychological state while driving”. Transport, 32(1), pp.13-22. 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