Analysis of Crash Precursors under Different Traffic Flow Conditions with Enhanced Interpretability

<div class="section abstract"><div class="htmlview paragraph">This study investigates the precursors of crashes under varying traffic states through an in-depth analysis of freeway traffic data. This method effectively addresses the limitations associated with using surrogate measures in traffic safety research. We used the k-means clustering method to categorize traffic states into three types: free flow, transitional state, and congested flow. By employing the case-control study experimental approach, we conducted an in-depth analysis of the traffic data. During the feature selection process, we set matching rules to choose control group data that meet the criteria of time, location, and traffic state. Initially, traffic flow feature variables were constructed based on multiple dimensions, including time window width, spatial location, traffic flow parameters, and statistical characteristics. To reduce feature multicollinearity, we used correlation matrices and variance inflation factors (VIF). We then applied Recursive Feature Elimination (RFE) combined with the XGBoost model to select key features, and interpreted the impact of these features on crash occurrence using the SHapley Additive exPlanations (SHAP) value. Finally, we employed a logistic regression model to evaluate the selected important features, reflecting the relationship between key features and crashes from a broad perspective. The results indicate significant differences in the main factors affecting crashes under different traffic conditions. In the free flow state, the relationship between the variability of flow and speed and crash occurrence is more significant. In the transitional state, the differences in vehicle distribution and speed across lanes significantly affect crashes; while in the congested flow state, the standard deviation of speeds among upstream lanes and the average flow of downstream have a greater impact on crashes. This study not only enhances the interpretability of traffic crash analysis methods but also provides a basis for traffic management departments to formulate corresponding traffic safety strategies for different scenarios.</div></div>