Analysis of factors and heterogeneity affecting injury severity in low-speed electric vehicle crashes involving older drivers

Abstract

Traffic safety risks of low-speed electric vehicles (LSEVs) among older drivers are a growing concern. Addressing limited existing research, this study investigates injury severity factors and unobserved heterogeneity using 1,346 older-driver LSEV crashes in Zibo, China (2022–2023). Following variable selection via Extreme Gradient Boosting (XGBoost) and Shapley Additive Explanations (SHAP), a random parameter logit model with mean and variance heterogeneity was developed. The model demonstrated superior goodness-of-fit compared to conventional models. Results show airbags, cardiovascular diseases, and vehicle type significantly affect injury severity. Licensing status and driver age were identified as significant random parameters, with several variables affecting their means and variances. This study comprehensively identifies risk factors, providing a scientific basis for targeted LSEV safety policies.