Analysis of factors influencing the severity of crashes between new-energy vehicles and pedestrians based on theoretical and data-driven models

With the increasing market share of new energy vehicles (NEVs), the incidence of NEV-related crashes has risen sharply, making the study of influencing factors of NEV crashes an urgent research priority. This study aims to identify and assess the key determinants affecting injury severity in NEV-pedestrian collisions to inform safety interventions. This study employs a hybrid method combining the SHAP approach, enhanced by information gain ratio, with random parameter logit model to explore the factors influencing the severity of NEV-pedestrian crashes in the UK from 2018 to 2022. The results show that the model’s predictive performance improved to 87.00% and 87.76% after random oversampling and hyperparameter optimization, respectively. The SHAP method, improved by the information gain weight, effectively distinguished the contribution rates of different influencing factors when multiple factors interact, significantly enhancing the differentiation of each factor’s importance. Pedestrian age was found to be the most significant characteristic factor, increasing the probability of severe injury by 7.82%. Turning (or preparing to turn) and lane changing or overtaking increased the likelihood of severe injury by 2.17% and 1.19%, respectively. Heterogeneous factors included female drivers, drivers aged 56 and older, pedestrians aged 46–65, pedestrians crossing without pedestrian facilities, and crashes occurring on weekends. By employing optimized data-driven models, theoretical models, and an improved SHAP method, a combined predictive model was constructed to predict and analyze the severity of NEV–pedestrian crashes. The findings of this study provide a foundation for formulating NEV collision safety strategies to reduce the severity of future collisions.