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.

将碰撞原因理解到采取对策所需的程度一直是交通安全领域的重点和难点。以前的研究受到碰撞数据不足和更适合单次碰撞的分析方法的限制。使用碰撞和接近碰撞 (CNC) 和自然驾驶研究可以帮助解决数据问题，使用碰撞前场景可以识别给定场景的多次碰撞中的高流行原因。因此，本研究提出了一种基于碰撞前情景的两阶段碰撞因果分析方法和一个系统地分类和分析影响因素的碰撞因果推导框架。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) 的碰撞前情景类型分为 23 个不同的碰撞前情景。进行了深入调查，并使用所提出的框架分析了共享相同场景的 CNC，该框架根据框架的道路使用者、车辆、道路基础设施和道路环境子系统的交互识别因果模式。通过统计分析，比较了三种常见的发生率最高的碰撞前情景的因果模式及其影响因素：追尾、变道和车辆-脚踏车。低速跟车时的制动错误、跟车太近以及与驾驶无关的分心是追尾情况的重要原因。在变道场景中，主要的成因模式包括非法使用转向信号灯和危险的车道变换作为关键因素。骑自行车的场景尤其受到视觉障碍物、非机动车车道不足以及骑自行车的人违反交通规则的影响。基于已识别的因果模式及其影响因素，针对三种常见情况提出了对策，为安全改进项目和高级驾驶辅助系统的开发提供支持。