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The aim of this study is to identify the psycho-social determinants of truck overloading from a behavioral perspective. Although there has been sizable efforts to investigate overloading from different perspectives, factors influencing truck drivers' decisions to overload have not been revealed. In this study, sampling 552 truck drivers, we used the theory of planned behavior (TPB), extended with risk perception (RP), facilitating circumstance (FC) and law enforcement knowledge (LEK), as a lens to examine overloading. A survey questionnaire was designed to collect data, including information regarding drivers' demographics and TPB variables. The results showed strong support for the application of the TPB in explaining overloading behavior. Specifically, behavioral intentions (BI) had the greatest direct positive effect on overloading behavior, followed by LEK, while perceived behavioral control had no direct positive effect. Interestingly, subjective norms had a significant positive effect on BI, while attitudes had a positive indirect effect, indicating that drivers' negative attitudes toward overloading could not alone reduce this risky violation. Furthermore, all extended TPB variables were significantly and positively associated with overloading intentions, with FC having the largest positive effect. Finally, these findings were discussed in view of determining how to make truck drivers less inclined to overload. [ABSTRACT FROM AUTHOR]

Driving behavior is the core of traffic simulation modeling. Despite the sizable efforts to investigate the influence of risky driving behavior on traffic flow, very little is known about its influence under car–truck heterogeneous traffic conditions. Therefore, this study aims to analyze the influence of several common risky driving behaviors on car–truck heterogeneous traffic flow. A two-lane cellular automata (CA) model for heterogeneous traffic flow consisting of cars and trucks was proposed, the conditions for the occurrence of dangerous situations (DSs) and traffic congestion were defined. Then the vehicle performance under the influence of risky driving behaviors prevalent in car–truck heterogeneous traffic was obtained and incorporated into the proposed CA model to measure their influence on crash probabilities and congestion rates. The simulation results indicate that risky driving behaviors have a significant negative influence on traffic safety and efficiency, increasing the frequencies of DSs, reducing the traffic flow and aggravating the traffic congestion. In addition, the combinations of risky driving behaviors in traffic flow further deteriorate the traffic safety and efficiency. For example, when two risky driving behaviors, such as distracted driving and aggressive lane-changing, affect the traffic flow simultaneously, the frequencies of DSs increase dramatically and in some density ranges, they are even higher than the sum of the frequencies caused by two single risky driving behaviors. These findings may have notable practical implications in developing policies and regulations that enable truck operations without compromising safety and efficiency. [ABSTRACT FROM AUTHOR]

This study aimed to exhibit the crash distribution and compare the contributory factors (crash characteristics, driver characteristics, vehicle characteristics and road characteristics) responsible for road crashes between non-commercial and commercial vehicles. To achieve the objective, a step-wise binary logistic regression (LR) model was employed with the forward LR method to explore the contributing factors to road crashes between the non-commercial and commercial vehicles. The road crash data (2013–2017) on motorways (M1 and M2) was collected from the National Highway and Motorway Police (NHMP) in Pakistan. During the study period, a total of 1110 road crashes were recorded. The proportion of fatal and non-fatal crashes were 29% and 71% for non-commercial vehicles and 31% and 69% for commercial vehicles, respectively. The results from LR model revealed that drowsy driving, poor road conditions, overspeeding and tire bursting were found to be significant predictors of road crashes. Road crashes caused by drowsy driving and poor vehicle condition were prevalent in commercial vehicles. On the contrary, overspeeding and tire bursting were more prevalent in non-commercial vehicles. The remaining factors could not achieve significant values in the model. On the basis of these empirical findings, suggestions to improve safety were pointed out. [ABSTRACT FROM AUTHOR]