This study investigates the utility of machine learning (ML) in understanding and mitigating winter road risks. Despite their capability in managing complex data structures, ML models often lack interpretability. We address this issue by integrating Shapley Additive exPlanations (SHAP) with a support vector machine (SVM) model. Utilizing a comprehensive dataset of 231 snowstorm events collected in the city of Edmonton across two winter seasons, the SVM model achieved an accuracy rate of 87.2%. Following model development, a SHAP summary plot was employed to identify the contribution of individual features to collision predictions—an insight not achievable through ML alone. Next, SHAP waterfall plots were used to assess the reliability of individual predictions. The findings enhanced our understanding of the complex SVM model and provided greater insights into the diverse factors affecting winter road safety.

This study investigates the application of machine learning (ML) to understand and mitigate winter road risks while addressing model interpretability. Using 26,970 winter crash records collected over four years in Edmonton, Canada, we developed and compared three ML-based winter crash frequency models: XGBoost, Random Forest, and LightGBM. To enhance interpretability, we applied SHapley Additive exPlanations (SHAP), providing insights into feature contributions. Our analysis incorporated micro-level variables such as collision records, weather conditions, and road characteristics, as well as macro-level variables such as land use patterns, spatial characteristics (via Hot Spot Analysis), and traffic exposure (estimated using Ordinary Kriging). Among the models tested, XGBoost outperformed others, achieving a testing R² of 92.67%, MAE of 3.64, and RMSE of 5.77. SHAP analyses on XGBoost provided both global and local explanations. At a global level, road type, speed limit, and traffic enforcement cameras were identified as key factors influencing crash frequency while locally, distinct features of high- and low-crash locations were highlighted, supporting targeted risk mitigation strategies. By bridging the gap between model accuracy and interpretability, this study demonstrates the value of interpretable ML models in improving winter road safety, offering actionable insights for informed decision-making and resource allocation in winter road maintenance.