School of Transportation Engineering, Tongji University, Shanghai 201804, China; The Key Laboratory of Road and Traffic Engineering, Ministry of Education, Shanghai 201804, China; National Engineering Laboratory for Integrated Optimization of Road Traffic and Safety Analysis Technologies, 88 Qianrong Rd, Wuxi 214151, China.
School of Transportation Engineering, Tongji University, Shanghai 201804, China; The Key Laboratory of Road and Traffic Engineering, Ministry of Education, Shanghai 201804, China.
Accid Anal Prev. 2022 Nov;177:106821. doi: 10.1016/j.aap.2022.106821. Epub 2022 Aug 30.
Understanding crash causation to the extent needed for applying countermeasures has always been a focus as well as a difficulty in the field of traffic safety. Previous research has been limited by insufficient crash data and analysis methods more suitable to single crashes. The use of crashes and near crashes (CNCs) and naturalistic driving studies can help solve the data problem, and use of pre-crash scenarios can identify the high-prevalence causes across multiple crashes of a given scenario. This study therefore proposes a two-stage crash causation analysis method based on pre-crash scenarios and a crash causation derivation framework that systematically categorizes and analyzes contributing factors. From the Shanghai Naturalistic Driving Study (SH-NDS), 536 CNCs were extracted, and were grouped into 23 different pre-crash scenarios based on the National Highway Traffic Safety Administration (NHTSA) pre-crash scenario typology. In-depth investigations were conducted, and CNCs sharing the same scenario were analyzed using the proposed framework, which identifies causation patterns based on the interaction of the framework's road user, vehicle, roadway infrastructure, and roadway environment subsystems. Through statistical analysis, the causation patterns and their contributing factors were compared for three common pre-crash scenarios of highest incidence: rear-end, lane change, and vehicle-pedalcyclist. Braking error in low-speed car following, following too closely, and non-driving-related distraction were important causes of rear-end scenarios. In lane change scenarios, the main causation patterns included illegal use of turn signals and dangerous lane changes as critical factors. Pedalcyclist scenarios were particularly impacted by visual obstructions, inadequate lanes for non-motorized vehicles, and pedalcyclists violating traffic regulations. Based on the identified causation patterns and their contributing factors, countermeasures for the three common scenarios are suggested, which provide support for safety improvement projects and the development of advanced driver assistance systems.
理解导致事故的原因，以便采取相应的对策，一直是交通安全领域的重点和难点。以前的研究受到数据不足和更适合单一事故分析方法的限制。利用碰撞和近碰撞（CNC）以及自然驾驶研究可以帮助解决数据问题，而使用碰撞前场景可以识别给定场景中多个碰撞的高发原因。因此，本研究提出了一种基于碰撞前场景的两阶段碰撞原因分析方法和一种碰撞原因推导框架，该框架系统地对影响因素进行分类和分析。本研究从上海自然驾驶研究（SH-NDS）中提取了 536 个 CNC，并根据美国国家公路交通安全管理局（NHTSA）碰撞前场景分类法将其分为 23 种不同的碰撞前场景。通过深入调查，利用提出的框架对共享同一场景的 CNC 进行了分析，该框架基于框架的道路使用者、车辆、道路基础设施和道路环境子系统的相互作用，确定了因果关系模式。通过统计分析，对发生率最高的三个常见碰撞前场景（追尾、变道和车辆-骑车人）的因果关系模式及其影响因素进行了比较。低速跟车时的制动失误、跟车过近和与驾驶无关的注意力分散是追尾场景的重要原因。在变道场景中，主要的因果关系模式包括非法使用转向灯和危险变道作为关键因素。骑车人场景特别受到视觉障碍物、非机动车辆车道不足和骑车人违反交通规则的影响。基于识别出的因果关系模式及其影响因素，针对这三个常见场景提出了对策，为安全改善项目和先进驾驶辅助系统的开发提供了支持。
