SHAP-based interpretation of holistic traffic risk causation patterns using high-resolution trajectory data and risk field theory

Using SHAP analysis, we interpreted the contribution of individual traffic state variables and their interactions to traffic risk. The vehicle type distribution in traffic flow was identified as a key factor influencing conflict occurrence. This study not only provides accurate risk prediction but also enhances the interpretability of machine learning-based traffic risk models, bridging prediction and causal insight.