A Two-Layer Stacking Model for Expressway Traffic Accident Rate Prediction: Leveraging Neural Networks and Tree-Based Models

Given the high casualty rate on expressways, this study aimed to accurately predict traffic accident rates and the key factors influencing them. Taking an expressway in Southern China as the research object, we constructed a two-layer stacking model integrating neural networks and tree models, based on accident data, traffic flow data, and road segment characteristic data. Six base models were integrated for prediction, and the Shapley Additive exPlanations (SHAP) method was used to analyze influencing factors. Results showed that the proposed model achieved the best performance, with a root mean square error (RMSE) of 11.05 and a mean absolute error (MAE) of 6.12, and its performance was significantly superior to that of other models (p < 0.05). Results from hyperparameter optimization and 5-fold cross-validation indicated that the proposed model had an RMSE of 8.91 ± 2.03, which was better than that of other models. Among all input factors, the proportion of tunnel length to total length and the variance of bridge width had the most significant impact on the expressway traffic accident rate, while the average width of tunnels had the lowest impact. This study realizes accurate prediction using widely available data and clarifies key factor mechanisms, providing support for expressway safety management and early risk warnings.