E-bike agents: Large Language Model-Driven E-Bike Accident Analysis and Severity Prediction

2025/06/05 by Zhichao Yang, Jingsha He, Yang, Zhichao +7
Engineering · Medicine · Social Sciences · #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Injury Epidemiology and Prevention #Traffic and Road Safety #Urban Transport and Accessibility

paper · pdf · doi:10.48550/arxiv.2506.04654

openalex publication_date 2025/06/05 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

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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