Traffic accident severity prediction based on an enhanced MSCPO-XGBoost hybrid model

Road traffic accidents pose a significant threat to public safety in China. This study proposes a novel severity prediction framework based on a Modified Stochastic Crested Porcupine Optimizer (MSCPO) combined with the XGBoost algorithm. The model was trained on 4287 accident cases from China's National Automobile Accident In-depth Investigation System (NAIS), collected between 2018 and 2023. The dataset was first divided into training and testing sets, and the Synthetic Minority Oversampling Technique (SMOTE) was applied only to the training set to address class imbalance. The MSCPO algorithm was then employed to optimize XGBoost hyperparameters. Comparative experiments demonstrate that the MSCPO-XGBoost model outperforms baseline algorithms including SVM, Random Forest, BP Neural Network, and CNN, achieving an accuracy of 83.57%, a recall of 85.23%, an F1-score of 84.30%, and an AUC of 92.82%. To enhance interpretability, SHAP analysis was used to identify key predictors such as engine displacement, vehicle mass, traffic signals, and driver age. The findings offer valuable guidance for traffic safety policymaking and demonstrate the potential of integrating real-time severity prediction into intelligent traffic management systems.