Injury severity analysis of e-bike crashes: An age-stratified study of riders aged 40 and above

As electric bikes (e-bikes) gain popularity, traffic safety concerns have intensified, particularly for riders aged 40 and above, who face heightened risks due to declining physiological capabilities. However, research analyzing crash injury severity factors for this demographic remains limited. This study examined 2452 e-bike crashes involving riders aged 40 and above in Jiaozhou, China, divided into three groups: 40-50 years, 50-60 years, and 60 years and above. A hybrid methodological framework combining the eXtreme Gradient Boosting (XGBoost) algorithm with Shapley Additive exPlanations (SHAP) and a Random Parameters Binary Logit model with Heterogeneity in Means (RPBL-HM) was constructed. Results showed that rural areas, primary/secondary roads, and holidays increase severe injury likelihood across all riders aged 40 and above. Each age group exhibited distinct risk patterns. The 40-50 age group showed higher severe injury probability with sub-zero temperatures and truck-involved crashes. The 50-60 age group faced elevated risks during nighttime, dawn, rainy or snowy weather, sub-zero temperatures, unhealthy air quality, and weekday nights. The 60 and above age group demonstrated higher risks when riders were farmers, unhealthy air quality, off-peak hours, motorcycle/truck involvement, rural autumn, and autumn crashes involving trucks. These findings provide evidence for developing age-targeted traffic safety interventions, offering significant implications for improving e-bike safety among elderly riders in an increasingly aging society.