Abstract
Traffic collisions are one of the most important challenges threatening the general health of the world. Iran’s crash statistics demonstrate that approximately 16,500 people lose their lives every year due to road collisions. According to the traffic police of Iran, heavy vehicles (including trailers, trucks, and panel trucks) contributed to 20.5% of the fatal road traffic collisions in the year 2013. This highlights the need for devoting special attention to heavy vehicle drivers to further explore their driving characteristics. In this research, the effect of heavy vehicle drivers’ behavior on at-fault collisions over three years has been investigated with an innovative approach of structural equation modeling (SEM) and Bayesian Network (BN). The database utilized in this research was collected using a questionnaire. For this purpose, 474 heavy vehicle drivers have been questioned in the Parviz Khan Border Market, located on the border of Iran and Iraq. The response rate of the survey was 80%. The participants answered the questions on Driver Behavior Questionnaire (DBQ) and a sleep assessing questionnaire named Global Dissatisfaction with Sleep (GSD). In this research, human factors affecting at-fault collisions of heavy vehicles were identified and their relationships with other variables were determined using the SEM approach. Then the descriptive model constructed by the SEM method was used as the basis of the BN, and the conditional probabilities of each node in the BN were calculated by the database collected by the field survey. SEM indicates that other attributes including GSD, mobile usage, daily fatigue, exposure, and education level have an indirect relation with heavy vehicle drivers’ at-fault collisions. According to the BN, if there is no information about the characteristics of a heavy vehicle driver, the driver will likely have at least one collision during the next three years with the probability of 0.17. Also, it was indicated that the minimum probability of the at-fault collision occurrence for a heavy vehicle is 0.08.
Highlights
Traffic collisions have become one of the most important challenges threatening the general health of the world [1]
Among driving behavior variables, only the variable error had a meaningful relationship with the number of traffic collisions, and the remaining cases were not identified as a variable that could describe traffic collisions, and, as shown in Figure 2, they indirectly affected atfault collisions. e study of the quality and accuracy of the Structural Equation Modeling (SEM) model has many and varied indicators evaluating each part of the model
Bayesian Network (BN) was used to answer the question of how likely heavy car drivers in Iran were likely to be involved in a collision. e problem we were faced within the first step was how to determine the relationships between the variables affecting the incidence of collisions. e relationship was determined using SEM in the previous section
Summary
Traffic collisions have become one of the most important challenges threatening the general health of the world [1]. In a global status report on road safety 2018, the World Health Organization has predicted that 16,426 people had died due to traffic collisions in Iran in the year 2016 [3]. Heavy vehicles (such as trailers, trucks, and panel trucks) constitute 8.3% of the total number of vehicles in Iran [4], while these vehicles were present in 20.5% of the fatal road traffic collisions in 2013. Given the high contribution of heavy vehicles to fatal road traffic collisions and the much smaller number of these vehicles compared to the total number of vehicles in Iran, the collision risk of this type of vehicle is way higher than that of cars that has led to a much higher drop in this category of cars than on riding cars, indicating the necessity of conducting a separate study on this category of drivers. Researchers divide the factors affecting the collisions into four general categories:
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