Mobile phone use among truck drivers: The application and extension of the theory of planned behavior.

Commercial truck drivers are particularly exposed to the risks associated with distracted driving, especially with mobile phone use while driving (MPWD), due to their higher driving exposure (DE) (e.g., high driving frequency, long driving hours and distance). However, despite being identified as one of the major causes in truck crashes, truck drivers' MPWD behavior has received little attention. In the current work, the theory of planned behavior (TPB), extended with DE, was applied to explore the determinants of MPWD among commercial truck drivers in China and examine the correlations between drivers' DE and psychological factors. We conducted an Internet survey and collected 420 valid questionnaires, which measured truck drivers' 5 standard TPB variables, DE and demographics. Structural equation modelling was used to analyze the data from the survey. The results showed strong support for the application of the proposed TPB model in explaining truck drivers' MPWD behavior. Specifically, truck drivers' behavioral intention (BI) had the greatest direct positive effect on MPWD behavior, while perceived behavioral control (PBC) had no direct positive effect. Moreover, PBC, attitude (ATT) and DE were significantly and positively associated with BI, while subjective norm was insignificant. As expected, DE has significant positive effects on truck drivers' psychological factors underlying MPWD behavior, especially on ATT and PBC, indicating that truck drivers with higher DE tend to have more positive attitudes toward MPWD and feel more confident about performing this risky behavior. These results may have notable practical implications in providing theoretical support for management and intervention of commercial truck drivers.

A study on the probabilistic quantification of heavy-truck crash risk under the influence of multi-factors.

In order to manage and reduce a specific risk, its quantitative analysis is necessary. The key objective of this study is to illustrate the prevalence of multi-factors in fatal crashes involving heavy trucks and to quantify the crash risk under the influence of multi-factors. Data from a recent, nationally representative sample of fatal crashes was investigated to identify the risk factors contributing to crash causations and a novel risk index was obtained to develop a criterion for crash risk quantification. Then, based on the mutual information theory, the mutual dependence between risk factors was calculated to quantify the crash risk under different risk factor combinations. The results reveal that most heavy-truck fatal crashes are the result of co-occurring multi-factors rather than a single factor, and are mainly caused by simultaneous occurrence of two or three contributing factors. Moreover, crash risk increases with the increase of the number of risk factors influencing the driver. Furthermore, multi-factor interaction between certain risk factors, such as environmental and vehicular factors, makes incremental contribution to the crash risk by further increasing the crash probability. Specifically, when driver's aberrant behaviors (errors and/or violations) are exposed to both environmental and vehicular factors, driver's likelihood of being involved in a fatal crash increases significantly. This suggests that in addition to the number of risk factors, the crash risk also depends on the multi-factor interactions between different risk factors. Therefore, the effects of individual risk factors should be controlled at the outset to prevent the incremental effects of multi-factors on crash risk, in turn enabling risk minimization..

Which factors contribute to road crashes in non-commercial and commercial vehicles? An examination of administrative data from motorways in Pakistan.

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.