Personal Mobility Devices (PMDs) have witnessed an extraordinary surge in popularity, emerging as a favored mode of urban transportation. This has sparked significant safety concerns, paralleled by a stark increase in PMD-involved crashes. Research indicates that PMD user behavior, especially in urban areas, is crucial in these crashes, underscoring the need for an extensive investigation into key factors, particularly those causing fatal/severe outcomes. Remarkably, there exists a noticeable gap in the research concerning the analysis of determinants behind fatal/severe PMD crashes, specifically in PMD rider-at-fault collisions. This study addresses this gap by identifying uniform groups of PMD rider-at-fault crashes and investigating cluster-specific key factor associations and determinants of fatal/severe crash outcomes using Seoul's PMD rider-at-fault crash data from 2017 to 2021. A comprehensive two-step framework, integrating Cluster Correspondence Analysis (CCA) and Association Rules Mining (ARM) techniques is employed to segment PMD rider-at-fault crash data into homogeneous groups, revealing unique risk factor patterns within each cluster and further exploring the combination of factors associated with fatal/severe PMD rider-at-fault crash outcomes. CCA revealed three distinct groups: PMD-vehicle, PMD-pedestrian, and single-PMD crashes. From the ARM, it was found that fatal/severe crashes were linked to dry road conditions, male PMD users, and weekdays, irrespective of the cluster. Whereas speeding violations and side collisions were associated with fatal/severe PMD-vehicle rider-at-fault crashes, traffic control violations were related to fatal/severe PMD-pedestrian rider-at-fault crashes at pedestrian crossings. Unsafe riding practices predominantly caused single-PMD crashes during daytime hours. From the findings, engineering improvements, awareness campaigns, education, and law enforcement actions are recommended. The new insights gleaned from this research provide a foundation for informed decision-making and the implementation of policies designed to enhance PMD safety.

中文翻译：

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与个人移动设备骑手过错碰撞中致命/严重碰撞结果相关的关键风险因素：两步集群间规则挖掘技术

个人移动设备 (PMD) 的受欢迎程度急剧上升，成为一种受欢迎的城市交通方式。这引发了严重的安全问题，同时 PMD 相关事故的急剧增加。研究表明，PMD 用户行为（尤其是在城市地区）在这些事故中至关重要，这凸显了对关键因素进行广泛调查的必要性，特别是那些导致致命/严重后果的因素。值得注意的是，在致命/严重 PMD 碰撞事故背后的决定因素分析方面，尤其是 PMD 骑手过失碰撞事故的研究中存在明显的差距。本研究通过使用 2017 年至 2021 年首尔 PMD 骑手过错碰撞数据来确定 PMD 骑手过错碰撞事故的统一群体，并调查特定集群的关键因素关联和致命/严重碰撞结果的决定因素，从而解决了这一差距。采用两步框架，集成聚类对应分析（CCA）和关联规则挖掘（ARM）技术，将 PMD 过错骑手碰撞数据分割为同质组，揭示每个聚类内独特的风险因素模式，并进一步探索与致命/严重 PMD 骑手过失碰撞结果相关的因素。CCA 揭示了三个不同的组：PMD 车辆事故、PMD 行人事故和单人 PMD 事故。ARM 发现，致命/严重碰撞事故与干燥路况、男性 PMD 用户和工作日有关，与集群无关。超速违规和侧面碰撞与致命/严重 PMD 车辆过错事故相关，而交通管制违规则与人行横道处致命/严重 PMD 行人过失碰撞事故相关。不安全的骑行行为主要导致白天发生单人 PMD 事故。根据调查结果，建议采取工程改进、宣传活动、教育和执法行动。从这项研究中收集到的新见解为明智的决策和旨在提高 PMD 安全性的政策的实施奠定了基础。