Abstract

Most accidents are directly related to driving offenses, and drivers who commit more offenses are more prone to accidents. Therefore, reducing driving offenses can reduce accidents. In other words, the recognition of common driving offenses among heavy vehicle (truck) drivers and the effective factors in directing them to reduce driving offenses can consequently reduce the frequency and severity of accidents. It seems that there is a necessity for in-depth studies to carry out research on this topic. The main objective of this study is to identify and evaluate important factors affecting lorry drivers committing traffic offenses. To achieve the goals, the required information was categorized into six categories: traffic tonnage, not fastening the seatbelt, speeding, technical defect, talking on cell phone, and lacking towing worksheet; these factors are known as dependent variables. Also, its influencing factors—in the group of driver characteristics, vehicle, and mileage—were obtained by using a demographic questionnaire, Driving Behavior Questionnaire (DBQ), and interviews with 420 drivers over 60 days at Tehran Terminal. After correcting incomplete questionnaires, 351 drivers’ information was used for statistical analysis. The statistical analysis of data using a multivariate logistic regression model showed that drivers loading and unloading five or six times per month are less likely to commit overloading than drivers loading and unloading more than 12 times per month. The results also show that the distracted drivers with less slip behavior are less likely to commit unauthorized speed offenses and 85.4% are less likely to commit this violation. Finally, the statistical analysis showed that drivers with aggressive driving behavior were more likely to commit a lack of towing worksheet offenses.

Highlights

  • Today, given the significant transportation activity in the countries’ GDP (Gross Domestic Product) and increasing the need for freight and cargo transportation, the importance of the freighter fleet has become increasingly bolder

  • According to the Iran Road Maintenance and Transportation Organization of the Ministry of Roads and Urban Development report in 2017, the volume of goods shipped within the country equals 428.348 million tonnes and the volume of journeys by trucks is 29.909 million, which has released the index of 224.836 million tonne-kilometers of freight [1]

  • Drivers’ offenses have been one of the major human factors leading to traffic accidents, which have been used in many studies to investigate driving behaviors

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Summary

Introduction

Given the significant transportation activity in the countries’ GDP (Gross Domestic Product) and increasing the need for freight and cargo transportation, the importance of the freighter fleet has become increasingly bolder. According to the Iran Road Maintenance and Transportation Organization of the Ministry of Roads and Urban Development report in 2017, the volume of goods shipped within the country (annually) equals 428.348 million tonnes and the volume of journeys by trucks is 29.909 million, which has released the index of 224.836 million tonne-kilometers of freight [1]. According to the Iran Road Maintenance and Transportation Organization of the Ministry of Roads and Urban Development report in 2017, there were 63472 registered violations of which 121108 led to suburban accidents, 16201 led to death, and 33595 led to injury [1]. Lorry drivers due to the different sizes and weights of the vehicle as well as the higher percentage of traffic on the roads as a group of professional drivers have great importance in reducing traffic offenses and subsequent. In spite of the importance and necessity of violations control in the cargo fleet mentioned in the introductory section, so far few studies have been conducted in this regard and most of them have used accidents as a parameter to predict accidents or only investigate the relationship between violations and demographic characteristics and the driver sleeping status

Literature Review
Materials and Methods
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Results and Analysis
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