School of Highway, Chang'an University, Xi'an, Shaanxi, China.
Innovation Research Institute of Shandong High-Speed Group, Jinan, Shandong, China.
PLoS One. 2022 Oct 27;17(10):e0276817. doi: 10.1371/journal.pone.0276817. eCollection 2022.
Roadway multi-fatality crashes have always been a vital issue for traffic safety. This study aims to explore the contributory factors and interdependent characteristics of multi-fatality crashes using a novel framework combining association rules mining and rules graph structures. A case study is conducted using data from 1068 severe fatal crashes in China from 2015 to 2020, and 1452 interesting rules are generated using an association rule mining approach. Several modular rules graph structures are constructed based on graph theory to reflect the interactions and patterns between different variables. The results indicate that multi-fatality crashes are highly associated with improper operations, passenger overload, fewer lanes, mountainous terrain, and run-off-the-road crashes, representing the key variables of factors concerning driver, vehicle, road, environment, and accident, respectively. Furthermore, crashes involving different severity levels, road categories, and terrain are verified to possess unique association rules and independent crash patterns. Moreover, the proportion of severe crashes caused by a combination of human-vehicle-road-environment factors (43%) is much higher than that of normal crashes (3%). This study reveals that the hidden associations between various factors contribute to the overrepresentation and severity of multi-fatality crashes. It also demonstrates that the crash mechanisms involving multi-fatality crashes and their interactions are more complex at the system level than those for normal crashes. The proposed framework can effectively map the intrinsic link between multiple crash factors and potential risks, providing transportation agencies with helpful insights for targeted safety measures and preventive strategies.
道路多人死亡事故一直是交通安全的重要问题。本研究旨在利用关联规则挖掘和规则图结构相结合的新框架，探索多人死亡事故的促成因素和相互依存特征。使用 2015 年至 2020 年中国 1068 起严重致命事故的数据进行了案例研究，使用关联规则挖掘方法生成了 1452 条有趣规则。根据图论构建了几个模块化规则图结构，以反映不同变量之间的相互作用和模式。结果表明，多人死亡事故与不当操作、乘客超载、车道较少、山区地形和驶出路外事故高度相关，分别代表了与驾驶员、车辆、道路、环境和事故相关的因素的关键变量。此外，还验证了涉及不同严重程度、道路类型和地形的事故具有独特的关联规则和独立的事故模式。此外，由人-车-路-环境因素组合引起的严重事故比例（43%）远高于普通事故（3%）。本研究揭示了各种因素之间隐藏的关联导致了多人死亡事故的过度表现和严重性。它还表明，涉及多人死亡事故及其相互作用的事故机制在系统层面上比普通事故更为复杂。所提出的框架可以有效地映射多个事故因素之间的内在联系和潜在风险，为交通管理部门提供有针对性的安全措施和预防策略的有用见解。
