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.

高风险驾驶员更有可能卷入交通事故，并且驾驶员的驾驶风险水平可能会受到许多潜在因素的影响，例如人口统计和个性特征。基于结构方程模型（SEM），本研究从战略公路研究计划2（SHRP 2）中抽取了3150名驾驶员，以探讨驾驶员的人口统计特征（性别，年龄和累积驾驶年）之间的关系，寻求感觉，风险感知和冒险驾驶行为。更具体地说，通过SEM构建了驾驶员特征对由性别控制的危险驾驶行为的中介模型。结果表明，驾驶经验对危险驾驶行为的影响部分由男性驾驶员的寻求感觉和风险感知所介导，而这些完全是由女性驾驶者的寻求感觉和风险感知所介导的。此外，随着女性驾驶员驾驶经验的增长，危险驾驶行为参与的发展趋势比男性驾驶员下降的幅度更大。最后，Random Forest分类器提出了驾驶员驾驶风险的分类模型，其中可以通过驾驶员的自我报告的人口统计信息，寻求感觉，风险感知和冒险驾驶行为。分类准确率可达到90％，这为识别潜在的高风险驾驶员提供了另一种方法，以减少交通事故造成的财产损失，伤害和死亡。