Safety assessment and influencing factors analysis of unsignalized intersections on national highways based on traffic conflicts

To address safety hazards at unsignalized intersections on national highways, this study proposes a probability-consequence-resolution (PCR) three-dimensional indicator system for traffic conflict classification and develops a multi-intersection joint safety assessment framework. Using road users’ trajectory data and macroscopic intersection features from four typical unsignalized intersections on national highways in Hebei Province, the study applies the proposed three-dimensional metrics in conjunction with the K-means clustering method to classify conflict severity levels. The persistence and evolution patterns of traffic conflicts were subsequently investigated. A safety assessment framework was established for individual conflicts and holistic intersection assessments, followed by XGBoost machine learning modeling to identify influencing factors. Key findings demonstrate: (1) Traffic conflicts were classified into four severity levels with dynamically adjustable classification thresholds; (2) Continuity was observed in conflict processes; (3) Conflict severity was primarily influenced by the overlap time of conflicting road users; (4) Both conflict count and intersection safety scores exhibited significant positive correlations with violating road users and traffic volume; (5) Among macroscopic features, road surface defects and the number of main-road lanes were identified as the dominant predictors. This methodology provides a new paradigm for safety diagnostics at unsignalized intersections.