Work

Electric motorcycle crash severity: a random parameters logit model considering interaction effects and counterfactual policy evaluation

Year: 2026

Type: article

Abstract: Electric motorcycles are highly vulnerable road users in complex roadside environments, yet the mechanisms governing their crash severity remain insufficiently understood. Using crash data from the UK STATS19 database (2020–2024), this study applies a random-parameters binary logit model with heterogeneity in means and variances (RPBL-HMV) to analyze the determinants of electric motorcycle crash severity, classified as serious injury or fatality versus slight injury. The results reveal substantial unobserved heterogeneity in environmental effects, with dry road surface identified as a key random parameter whose influence varies across crashes. Male riders, young riders, and high-class roads significantly increase the likelihood of severe injuries, whereas male casualties and junction locations are more strongly associated with slight injuries. Multi-vehicle crashes pose a higher severity risk than single-vehicle crashes due to more complex crash interactions. Electric motorcycle crashes also exhibit a distinct low-energy injury pattern, in which skidding, overturning, and crashes with roadway objects are frequent but generally associated with slight injury. Interaction effects further indicate elevated severity risks for crashes involving young male riders and male riders on high-class roads. Counterfactual simulations based on the variation amplitude of crash severity probability (VACSP) demonstrate that temporal interventions yield the greatest benefits, with morning-period risk management reducing severe injury probabilities, while police presence provides a stable mitigating effect. By accounting for unobserved heterogeneity and interaction effects, this study improves understanding of electric motorcycle crash severity and provides evidence to guide targeted traffic safety management and policy.

Source: Safety Science

DOI: 10.1016/j.ssci.2026.107206

Authors: Linwei Wang, Xiaohua Zhao, Yang Bian, Ying Yao, Bohan Liu

Institution: Beijing University of Technology

Language: English

Topic: Traffic and Road Safety

Subfield: Safety, Risk, Reliability and Quality

Field: Engineering

Domain: Physical Sciences

SDGs: Sustainable cities and communities, Good health and well-being, Industry, innovation and infrastructure

Open Access status: hybrid

Estimated APC paid: $4,360