Analysis of Injury Severity in Elderly Pedestrian Traffic Accidents Based on XGBoost

With declining physical functions, elderly pedestrians face a significantly higher risk of severe injuries and fatalities in traffic accidents. This study investigates the factors influencing injury severity among elderly pedestrians using traffic accident reports collected by the Shaanxi Chang’an University Traffic Accident Evidence Identification Center, covering nationwide cases from 2023 to 2024. By analyzing 2351 accident reports involving pedestrians aged 60 and above, 31 feature variables closely related to accident severity were selected to build a predictive model based on the XGBoost algorithm. Additionally, the SHAP method was employed to perform feature attribution analysis on the model’s key variables. The experimental results show that: (1) the model achieved 86% accuracy, 83% precision, 87% recall, and an F1 score of 85%, demonstrating the reliability of XGBoost in predicting injury severity among elderly pedestrians. (2) Global analysis identified collision speed, injury location, and driver awareness as the main factors influencing injury severity. However, the key factors differ across accidents of different severity levels. (3) The effect of the same factor also varies by severity level. For example, driver awareness reduces the likelihood of minor injuries but has less impact on severe injuries or fatalities. This study provides a theoretical foundation for developing traffic safety policies targeting elderly pedestrians and contributes to effectively reducing the severity of injuries in elderly pedestrian traffic accidents.