Factors Related to Crashes on Pavements Under Adverse Weather Conditions for Different Road Types: A Multi-Level Study Using Both Traditional Approach and Machine Learning

Crashes on pavements affected by adverse weather conditions are still of interest given that there is still room for infrastructure improvement, even if other influential factors should be considered. The main objectives of this study are the identification of the most relevant factors associated with crashes occurring on pavements affected by adverse weather conditions for different road types and the investigation of the relationships between those factors from the perspective of the Safe System approach. To reach these aims, a multi-level methodology was used, based on a traditional logistic regression model approach coupled with gradient boosting models interpreted based on variable importance and SHAP values for the first step, and association rule mining for the second step. The dataset for the most recent three-year period (2022–2024) of fatal+injury crashes that occurred in Italy was used. In both methodological steps, the results were differentiated by road jurisdiction (municipality, province, state, motorway agency). The findings include the identification of several factors associated with crashes that occurred under adverse pavement conditions, in some cases varying across road jurisdictions. Among them, the influence of horizontal curves, season, hour, crash types and heavy vehicles was particularly highlighted. Several relationships between crash factors were further identified and discussed from the perspective of the Safe System approach.