The mediating effect of driver characteristics on risky driving behaviors moderated by gender, and the classification model of driver ? s driving risk

High-risk drivers are more likely to be involved in traffic accidents, and the driving risk level of drivers could be affected by many potential factors, such as demographics and personality traits. Based on the Structural Equation Model (SEM), this study involves a sample of 3150 drivers from the Strategic Highway Research Program 2 (SHRP 2), to explore the relationships among drivers? demographic characteristics (gender, age, and cumulative driving years), sensation seeking, risk perception, and risky driving behaviors. More specifically, the mediation model of driver characteristics on risky driving behaviors moderated by gender is constructed by the SEM. The results show that the effects of driving experience on risky driving behaviors are partially mediated by sensation seeking and risk perception for male drivers, while those are completely mediated by sensation seeking and risk perception for female drivers. Moreover, the development trend of risky driving behavior engagements declines greater with the growing of driving experience for female drivers than male drivers. Finally, a classification model of the driver?s driving risk is proposed by the Random Forest classifier, in which the driving risk level of the driver evaluated by the crash and near-crash rate could be classified through the driver?s self-reported demographics, sensation seeking, risk perception, and risky driving behaviors. The classification accuracy achieves up to 90 percent, which offers an alternative approach to identifying potential high-risk drivers to reduce property losses, injuries, and death caused by traffic accidents.

Automated summary

The study explores the relationships between driving risk and driver demographics, sensation seeking, and risk perception, using a sample of 3150 drivers from SHRP 2. Through SEM analysis, it is found that the effects of driving experience on risky driving behaviors are mediated differently for male and female drivers. Additionally, a Random Forest classifier is proposed to classify driving risk levels based on self-reported information, achieving a classification accuracy of up to 90 percent.