School of Transportation, Jilin University, Changchun, China.
Jilin Research Center for Intelligent Transportation System, Changchun, China.
PLoS One. 2023 Oct 20;18(10):e0293307. doi: 10.1371/journal.pone.0293307. eCollection 2023.
Research on road safety has focused on analyzing the factors that affect crashes. However, previous studies have often neglected differences in crash causation among heterogeneous clusters of drivers. In particular, the differences in the combined effect mechanisms of the factors in the risk scenarios have not been completely explained. Therefore, this study used the K-means algorithm to perform multidimensional feature homogeneous clustering for drivers involved in crashes and near-crashes. Structural equation modeling involving mediating effects was introduced to explore the direct and indirect effects of each influencing factor on vehicle crashes under risk scenarios and compare the differences in crash causation among different driver clusters. The results indicate that the drivers who experienced the risk scenarios can be classified into two homogeneous driver clusters. Significant differences exist in the demographic characteristics, intrinsic driving characteristics, and crash rates between them. In the risk scenario, traffic factors, distraction state, crash avoidance reaction, and maneuver judgment directly affect the crash outcomes of the two cluster drivers. Demographic characteristics and environmental factors have fewer direct influence on the crash outcomes of two-cluster drivers, but produce more complex mediating effects. Analysis of the differences in the influence of factors between clusters indicates that the fundamental cause of crashes for cluster 1 drivers includes poor driving skills. In contrast, cluster 2 drivers' crashes were more influenced by traffic conditions and their safety awareness. The analysis method of this study can be used to develop more targeted road safety policies to reduce the occurrence of vehicle crashes.
道路安全研究一直侧重于分析影响事故的因素。然而，以往的研究往往忽略了不同类型驾驶员群体在事故致因方面的差异。特别是，风险情景下各因素的综合影响机制的差异尚未得到充分解释。因此，本研究使用 K 均值算法对涉及事故和险肇事故的驾驶员进行多维特征同质聚类。引入中介效应结构方程模型，以探讨风险情景下各影响因素对车辆事故的直接和间接影响，并比较不同驾驶员群体的事故致因差异。结果表明，经历过风险情景的驾驶员可分为两个同质驾驶员群体。他们在人口统计学特征、内在驾驶特征和事故率方面存在显著差异。在风险情景下，交通因素、分心状态、避险反应和操作判断直接影响两个集群驾驶员的碰撞结果。人口统计学特征和环境因素对两个集群驾驶员碰撞结果的直接影响较小，但产生了更复杂的中介效应。对集群间因素影响差异的分析表明，集群 1 驾驶员碰撞的根本原因包括较差的驾驶技能。相比之下，集群 2 驾驶员的碰撞更多地受到交通状况和安全意识的影响。本研究的分析方法可用于制定更有针对性的道路安全政策，以减少车辆事故的发生。
