用大模型分析电单车事故，发现电池起火等独特风险
E-bike agents: Large Language Model-Driven E-Bike Accident Analysis and Severity Prediction
- 结合事故描述与人口数据，建立标准化分类框架
- 电单车事故中电池起火、刹车失灵更常见且伤情更重
- 为城市交通设计提供针对性安全干预依据
电动自行车作为可持续城市出行方式迅速普及，但其安全问题仍研究不足。本文利用CPSRMS和NEISS两个数据集，分析电动自行车与传统自行车的伤害事件。提出标准化分类框架，识别并量化伤害原因与严重程度。结合事故叙述与人口属性，揭示两者在机械故障模式、伤情严重程度及受影响人群上的差异。尽管两类车辆存在共同原因（如失控、踏板故障），但电动自行车具有电池起火、刹车失效等特有风险。研究结果凸显需针对微出行设备制定差异化安全干预措施与基础设施设计。
原文摘要 · Abstract (English)
E-bikes have rapidly gained popularity as a sustainable form of urban mobility, yet their safety implications remain underexplored. This paper analyzes injury incidents involving e-bikes and traditional bicycles using two sources of data, the CPSRMS (Consumer Product Safety Risk Management System Information Security Review Report) and NEISS (National Electronic Injury Surveillance System) datasets. We propose a standardized classification framework to identify and quantify injury causes and severity. By integrating incident narratives with demographic attributes, we reveal key differences in mechanical failure modes, injury severity patterns, and affected user groups. While both modes share common causes, such as loss of control and pedal malfunctions, e-bikes present distinct risks, including battery-related fires and brake failures. These findings highlight the need for tailored safety interventions and infrastructure design to support the safe integration of micromobility devices into urban transportation networks.
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