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1-Introduction

            In the past few decades due to urban expansion, mobility has become a key factor in traffic analysis that caused the significant increase of private car ownership, which highlights the human factor in car crashes. In 2012 the third major cause of death in Iran was Motor vehicle crashes (MVCs) 1. Many researchers indicate two major factors that keep crash rate high are aggressive driving and driver’s lack of attention. 2 Aggressive driving has not been defined clearly, so it covers many driving behaviors depending on the definitions used. National Highway Traffic Safety Administration (NHTSA) defines aggressive driving defines as “operating a motor vehicle in a manner that endangers or is likely to endanger persons or property “. This administration considers distracted driving as an example of aggressive driving behavior. According to this report, about 14 percent of drivers encounter inattention and distraction while driving. It appears drivers who are under 21 are more likely to engage such actions 3.

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 1-1 The Role of Selective Attention in Driving Tasks

          In order to analyze automatic behavior, driving is one of which, cognitive psychologist uses tasks, which include an automatic behavior contrasting another considered behavior. Using such tasks enable researchers to deeply analyze the automatic behavior. In 1935, Stroop suggested a test to analyze selective attention 4. Selective attention is the ability to pay attention to one special stimulus among other distractors. Some studies have proven that attentional bias which is a phenomenon in which despite efforts to ignore distracting stimuli attention is directed toward it is a good predictor of aggressive driving behavior 5. According to NHTSA, driving distraction is estimated to be one of the leading cause of motor vehicle crashes (MVC). Many studies have examined the role of driver’s distraction, that is eating, cellphone conversations, listening to music, involving in conversation, etc. 6. Although many studies have examined the role of attention in driving the predictability of the level of the selective attention as a tool to control MVCs is not fully understood. In 2017, Hatfield et al. examined the relationship between impulsive and risky stimulated driving among young drivers using Stroop task as a way to measure selective attention 7. Collet et al. used Stroop colored word test to study drivers’ performance while confronting a critical crash avoidance situation according to this study participants’ performance in Stroop test was the determining factor in the management such a situations 8. In 2014, Spengler et al. used Stroop test to predict driving mistake in a driving simulator. According to this research Stroop test was a significant predictor of driving mistakes while driving in a simulated driving environment 9. Babika et al. examined the relationship between performance in Stroop test and driving errors conducted in driving simulator. The results of this research indicated significant inverse relationship between Stroop performance and errors. 10

1-2 The Driving Simulation and Sources of Distraction

          Driver simulation has been used as a tool for investigating drivers’ behaviors for a long time 11, 12. This method provides a safe environment for evaluating the drivers’ behaviors under a variety of situations. Many studies have evaluated their hypothesis by measuring descriptive variables but in this study, quantitative variables were collected and analyzed beside descriptive ones. Malik et al. used a similar approach for this purpose 13.

Most of researchers categorized distracted driving as an aggressive driving behavior. In this study, the exacerbation of the aggressive driving due to distraction is being investigated as the hypothesis.

 

 2-Methodology

2-1 Subjects

          Fifteen male drivers aged between 22 to 27 years old with mean 23.73 and variance of 0.995, holding the grade-3 driving license, without any disability or history of psychiatric issues was selected randomly from a group of volunteers. All the volunteers had good eyesight and hearing ability by definition of traffic law and did not have to use any instruments or devices. All of them had experienced more than 1000 hours of driving. Because no volunteers had experienced simulated driving, a 10-minute long introductory practice was considered.

2-2 Self-stated questionnaire on personal characteristics

          The volunteers were asked to complete three tasks. The first task was included a self-stated questionnaire in order to identify gender, the frequency of driving, history of accidents and familiarity with simulated driving environments. This task was meant to indicate the volunteer’s characteristics and whether the volunteer is eligible for the experiment or not. The questionnaire is presented in Table 1.

 

Table 1: Self-stated questionnaire on personal characteristics

Answers

Questions

 

Gender

 

Age

Other

No License

Grade 1

Grade 2

Grade 3

Driving License:

When did you get your driving license?

I haven’t drive awhile

occasionally

few times a month

2-3 days a week

Daily

How often do you drive? (During last 12 month)

How many accidents did you have during last year, which cause fatalities?

 

How many accidents did you have during last year, which cause serious injuries?

 

How many accidents did you have during last year, which cause serious damage to any vehicle?

 

How many accidents did you have during last year, which cause minor damage to any vehicle?

 

Did you use simulated driving in last year?

 

2-3 Stroop Effect Test

          As introduced in the introduction, classic Stroop test determines attention level. To do so, classic Stroop test conducted in the volunteer’s native language by computer. The result was processed and the score was recorded for future analysis.

2-4 Car Simulation

For creating a simulated driving environment, City Car Driving software was used. This software provides a realistic simulated driving environment based on user-defined variables (Figure 1). This step was designed in accordance to the suggested procedure previously designed and practiced by Malik et al. 13.

 

Figure 1: City Car Driving Simulator

 

In order to prevent the effect of unfamiliarity with the simulated driving environment, a ten minutes’ introductory practice was provided. In this step, the volunteer was asked to drive in a simulated parking lot without any obstacle for 5 minutes. This part was meant to providing enough time for the volunteers to become familiar with the simulated car itself which means using the throttle, brake and clutch pedals, realizing car’s dimensions, controlling car in the simulated environment using the steering wheel and getting the sense about braking distance and the brakes efficiency. After this part, in order to practice simulated driving, volunteers were asked to drive in a route similar to the actual test routes for 5 minutes. At this step, the volunteer was told to obey Iran national traffic law, which he was familiar with before.

There are many studies considering different sources of distraction and their effect on driver’s behavior. In order to get a more accurate result from this set of tests, an environment with the least distracting elements was chosen. To reach this goal, a rural road, which contains road signs and one type of tree outside of road’s clear zone, was selected and the weather condition was adjusted to be sunny. The traffic volume was low with the occasional cluster of vehicles without any pedestrian around. Simulated traffic was consisting of mostly cautious drivers with few aggressive drivers, which is considered typical in most cities. All these parameters had been preserved during all of the steps.

At the second step, the volunteer was asked to drive in a simulated route. In this step, the baseline data was collected while there were no distracting stimuli.

In the third step, the driver was asked to listen carefully to a series of news from a Persian radio. The route characteristics were the same as the previous step. After reaching the destination, he was asked to answer some question about the content and detail of the news. The questions were either a single word question or a yes or no question. All the part was recorded in order to evaluate the relation between the driving behavior at the time that questions’ answers were provided.

In the last step, the volunteer was asked to drive to the third destination in a route with the similar condition to other steps’ routes. The parameters were collected using the same methods. Through this step 4 prepared questions were asked from the driver. These questions were considered neutral and do not encourage aggressive driving through emotion stimulation. These questions were either counting, naming or identifying objects inside the car or in the road. This step was meant to simulate the effect of a conversation between the driver and the passenger.

2-5 Data Collecting and Analysis Method

Although the software was automatically counting some of the aggressive driving parameters, an observer used recorded screen video to analyze these errors and behavior. The volunteer wasn’t aware of the counting method and parameters, which were being collected. It should be mentioned that the study was double side blind. In order to analyze the provided data, the Analysis of variance (ANOVA) was used in this research.

 3- Results

          By using analysis of variance (ANOVA), it is concluded that the driver’s speed will decrease as the distraction level increases. Volunteers with lower Stroop score are more likely to use road shoulder and overtake recklessly; it appears that the probability of using shoulder while driving does not have a relationship with the distraction level while driving. The driver’s attitude toward overtaking on the right appears to be constant until a specific point of distraction level for each driver at which the rate increased significantly.

          With regard to the driving distraction score of the volunteers, volunteers conducting right overtake error, had the significantly lower score in the third part of Stroop test. Regression analysis showed that Stroop test score is a good predictor of driving errors, including shoulder use and right overtake as much as 42%.

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