Analysis of factors affecting the severity of conflicts between right-turning heavy vehicles and non-motorized vehicles at road intersections

OBJECTIVE: To deconstruct the microscopic dynamic games and risk evolution mechanisms between right-turning heavy vehicles (HVs) and non-motorized vehicles (NMVs) at signalized intersections. METHODS: High-fidelity unmanned aerial vehicle (UAV) video data from three intersections in Nanjing were used to extract 527 conflict events and reconstruct five interaction patterns. Using time-to-collision (TTC) and post-encroachment time (PET) as surrogate safety measures, K-means clustering categorized conflict severity into potential, moderate, and severe levels. An ordered logit model (OLM) with interaction terms was developed to quantify the non-linear impacts of vehicle kinematics, behaviors, and environmental factors. RESULTS: HV speed and jerk (deceleration rate) are primary catalysts for severe conflicts. Microscopic behaviors exhibit a "double-edged sword" effect: proactive HV yielding reduces risk, but being "forced to stop" by NMVs during yielding increases severe conflict probability by 11.8%. A "safety in numbers" effect (>4 NMVs) decreased risk by 6.0%, while comprehensive safety facilities reduced severe conflict probability by 7.7%. CONCLUSIONS: This study reveals dynamic risk mechanisms for right-turning HVs, providing an empirical basis for optimizing intersection facility deployment and advanced driver assistance system (ADAS) warning strategies to enhance traffic safety.