Variables in the equation.

This study evaluates the effectiveness of Stopping for Right-Turning Large Vehicles Policy in Nanjing, designed to mitigate accidents attributed to blind spots and delayed braking of large trucks at intersections. Using high-resolution conflict data from four signalized intersections in Jiangning District, collected via unmanned aerial vehicles (UAVs) and roadside video, the research employs K-means clustering for conflict severity classification and binomial Logit regression to identify critical determinants. Results reveal the policy exhibited limited statistical significance in reducing severe conflicts (p > 0.05). Regression analysis quantified four critical determinants: absence of motorized/non-motorized segregation (OR=1.82, + 81.6% severity odds), elevated stop-line speeds (OR=1.32, + 31.9%), failure to yield (OR=2.45, + 145%), and crossing the street within the zebra crossing (OR=0.19, −81.0%). The analysis demonstrates that infrastructural deficiencies and behavioral non-compliance outweigh the policy’s standalone impact. Based on these findings, the study proposes a holistic optimization framework integrating physical separation measures, enhanced signage, dynamic traffic signal adjustments, and data-driven enforcement strategies. Methodologically, this study innovatively combines unsupervised learning for conflict categorization, providing a scalable framework for evaluating urban traffic policies. This research underscores the necessity of multi-dimensional interventions—spanning infrastructure, enforcement, and public education—to achieve sustainable improvements in intersection safety. The findings offer actionable insights for policymakers to refine regulatory measures and enhance road safety in rapidly urbanizing environments.