School of Transportation and Logistics, Southwest Jiaotong University, Chengdu Sichuan, China.
Institute of System Science and Engineering, Southwest Jiaotong University, Chengdu Sichuan, China.
Int J Inj Contr Saf Promot. 2024 Mar;31(1):12-29. doi: 10.1080/17457300.2023.2245804. Epub 2023 Aug 10.
Drawing on the core idea of Propensity Score Matching, this study proposes a new concept named Historical Traffic Violation Propensity to describe the driver's historical traffic violations, and combines the new concept with an improved mutual information-based feature selection algorithm to construct a method for screening key traffic violations from the perspective of expressing driver's accident risk. The validation analysis based on the real data collected in Shenzhen demonstrated that drivers' state of Historical Traffic Violation Propensity on 19 key traffic violations screened have a stronger predictive ability of their subsequent accidents compared to the level in existing research. The positive state of Historical Traffic Violation Propensity on 'Drinking', 'Parking in dangerous areas', 'Wrong use of turn lights', 'Violating prohibited and restricted traffic regulations', and 'Disobeying prohibition sign' will increase the probability of a driver's subsequent accident by more than 1.7 times. The research provides directions to more efficiently and accurately capture the driver's accident risk through historical traffic violations, which is valuable for identifying high-risk drivers as well as the key psychological or physical risk factors that manifest in daily driving activities and lead to subsequent accidents.
本研究借鉴倾向评分匹配的核心思想，提出了一个新的概念，即历史交通违规倾向，用以描述驾驶员的历史交通违规行为，并结合改进的基于互信息的特征选择算法，从表达驾驶员事故风险的角度构建了一种从关键交通违规行为中筛选的方法。基于深圳实际采集数据的验证分析表明，与现有研究中的水平相比，筛选出的 19 项关键交通违规行为中驾驶员历史交通违规倾向的状态对其后续事故具有更强的预测能力。历史交通违规倾向的正向状态在“饮酒”、“在危险区域停车”、“错误使用转向灯”、“违反禁行和限制交通规则”和“违反禁止标志”上会使驾驶员后续事故的概率增加 1.7 倍以上。该研究为更高效、准确地通过历史交通违规行为捕捉驾驶员的事故风险提供了方向，对于识别高风险驾驶员以及在日常驾驶活动中表现出来并导致后续事故的关键心理或生理风险因素具有重要价值。
