Analysis of Vehicle Crossing Conflict Severity at Unsignalized Intersections Based on Trajectory Data

Vehicle interactions at unsignalized intersections are complex, and crossing conflicts may differ considerably in their potential severity. This study develops a trajectory-based framework for identifying and analyzing vehicle crossing conflict severity using data from the Bendplatz intersection in the inD dataset. Post-encroachment time (PET) and time to the second conflict point (T2) are applied as complementary criteria to identify 370 crossing conflict pairs, which are classified into Minor, Moderate and Severe levels based on relative speed. Thirteen explanatory variables are constructed from the pre-conflict motion states of both vehicles, the road-priority relationship, and short-term traffic exposure. Five classification models are compared using nested stratified cross-validation. LightGBM achieves the best overall performance, with an Accuracy of 0.8162, a Macro-F1 of 0.7850, and a macro-averaged area under the receiver operating characteristic curve (MAUC) of 0.9326. Out-of-fold SHapley Additive exPlanations (SHAP) analysis shows that the average speeds of the first-passing and later-passing vehicles together account for 48.09% of the summed global variable importance, while the lateral-motion state of the later-passing vehicle also makes an important predictive contribution. SHAP interaction analysis further reveals nonlinear interaction patterns involving the speed states of both vehicles and the lateral adjustment of the later-passing vehicle. Sensitivity analyses using alternative severity thresholds and traffic-exposure windows show that the principal variable-importance pattern remains broadly stable. The proposed framework can support event-level conflict screening and provide quantitative evidence for subsequent field investigation and safety monitoring at unsignalized intersections.