Integrating visual factors in crash rate analysis at Intersections: An AutoML and SHAP approach towards cycling safety.

Cycling crashes constitute a significant and rising share of traffic accidents. Consequently, exploring factors affecting cycling safety has become a priority for both governmental bodies and scholars. However, most existing studies have neglected the vision factors capable of quantitatively describing the city-level cycling environment. Moreover, they have relied on limited models that lack interpretability and fail to capture the spatial variations in the contribution of factors. To address these gaps, this research proposed a framework that used origin-destination-based cycling flow and vision factors generated from Google Street View images to identify the leading factors. It also employed the comparative Automatic Machine Learning and interpretable SHAP value-based geospatial analysis to explain each factor's contribution to the cycling crash risk, with a particular focus on the spatial variations in the influence of vision factors. The effectiveness of this framework was validated by a case study in Manhattan, which examined the leading risk factors of cycling crash rates at intersections. The results showed that the LightGBM model, with selected subsets of factors, outperformed other models. Through SHAP explanations of global feature importance, the study identified the proportion of road barriers, the proportion of open sky, and the number of visible trucks as the leading visual risk factors. Additionally, using SHAP-based geospatial analysis, the study revealed the local variations in the effects of these three factors and identified eight areas with higher cycling crash rates. Based on these findings, the study provided practical measures for a safer cycling environment in Manhattan.