E-bike agents: Large Language Model-Driven E-Bike Accident Analysis and Severity Prediction
This work addresses safety implications for e-bike users and urban planners by providing insights into injury causes and severity, though it is incremental as it applies existing methods to new data.
This paper analyzed injury incidents involving e-bikes and traditional bicycles using CPSRMS and NEISS datasets, revealing that e-bikes have distinct risks like battery-related fires and brake failures compared to shared causes such as loss of control. The findings emphasize the need for tailored safety interventions and infrastructure design for micromobility devices.
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.