Interpretable modelling of pedestrian crashes at crosswalks considering holistic visual road environment characteristics.

Analyzing the factors influencing pedestrian crashes at crosswalks from both drivers' and pedestrians' visual perspectives is essential for improving pedestrian safety. However, current studies have not sufficiently considered the holistic visual characteristics of road environments and have provided limited practical guidance for crosswalk safety improvement. To address these gaps, this study integrates XGBoost with SHAP values to conduct an interpretable analysis of pedestrian crashes at crosswalks, considering holistic characteristics of the visual road environment. Appearance, depth, and color features were extracted from street-view images for 373 crosswalks, and hierarchical clustering was applied to classify them into three distinct visual clusters representing holistic environmental characteristics. These visual clusters were then combined with road design, crosswalk, traffic control, exposure, and contextual variables to develop an XGBoost model for predicting whether pedestrian crashes occurred at each crosswalk, achieving an accuracy of 0.866 and an AUC of 0.896. SHAP values were then used to identify the relative importance of each independent variable, the specific effects of individual variables, and the joint effects between variables. Road width and visual clusters emerged as important factors, with wider roads associated with a higher predicted crash likelihood and visually balanced environments associated with a lower predicted crash likelihood. Notable joint effects were also observed between road width and visual clusters. The proposed methodology helps provide practical insights for crosswalk design and pedestrian safety improvement.