Identifying Significant Variables Influencing Overtaking Maneuvers on Two-lane, Two-way Rural Roads in Iran

The main purpose of this study is examining effective and significant variables on overtaking maneuvers on two-lane, two-way rural roads in Iran. In this study, overtaking maneuver type as the response variable was considered in four levels: “normal overtaking (accelerative overtaking)”, “aborted overtaking” maneuver, “lane sharing” and “cutting in (precipitous return to the driving lane)”. The data were gathered using field data collection method, that is, an expert –a transportation engineer- accompanied by patrolling police interviewed 514 drivers on two-lane, two-way rural roads in two provinces of Zanjan and East Azerbaijan in the northwest of Iran. To identify the influence of each variable on the overtaking type, Pearson’s chi-square test with the significance level of 0.05 was used and then to consider the influence of each significant variable on each level of the response variable, a multivariate logistic regression model was employed

A Data Mining Approach on Lorry Drivers Overloading in Tehran Urban Roads

The aim of this study is to identify the important factors influencing overloading of commercial vehicles on Tehran’s urban roads. The weight information of commercial freight vehicles was collected using a pair of portable scales besides other information needed including driver information, vehicle features, load, and travel details by completing a questionnaire. The results showed that the highest probability of overloading is for construction loads. Further, the analysis of the results in the lorry type section shows that the least likely occurrence of overloading is among pickup truck drivers such that this likelihood within this group was one-third among Nissan and small truck drivers. Also, the results of modeling the type of route showed that the highest likelihood of overloading is for internal loads (origin and destination inside Tehran), and the least probability of overloading is for suburban trips (origin and destination outside of Tehran). Considering the type of load packing as a variable, the results of binary regression model analysis showed that the most probability of overloading occurs for packed (boxed) loads. Finally, it was concluded that drivers are 18 times more likely to commit overloading on weekends than on weekdays

Investigating the Effect of Urban New Technologies on the Iranian Lorry Drivers’ Behavior

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