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

TL;DR

This study analyzes injury data from e-bike and bicycle incidents to identify unique safety risks of e-bikes, emphasizing the need for tailored safety measures and infrastructure improvements.

Contribution

It introduces a standardized classification framework for injury causes and severity, integrating incident narratives with demographic data for detailed analysis.

Findings

E-bikes have distinct risks like battery fires and brake failures.

Both e-bikes and bicycles share common injury causes such as loss of control.

E-bike injuries vary across different user demographics.

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.