Abstract:

Aiming at the mechanism of traffic conflicts in urban expressway weaving areas under different weather conditions， a traffic conflict identification algorithm based on vehicle bounding box information was proposed. The peak over threshold extreme value theory was employed to determine severe conflict thresholds under distinct weather conditions， and machine learning models combined with interpretability analysis methods were utilized to systematically investigate the impact of weather on traffic conflict characteristics. By leveraging high-precision trajectory data， the limitations of traditional conflict metric extraction methods， such as insufficient accuracy， subjectivity in threshold selection， and the lack of exploration into weather-related influences were addressed. The results demonstrate that the severe conflict thresholds for sunny and rainy conditions are 1.75 s and 1.85 s， respectively， with the proportion of severe conflicts in rainy conditions （39.1%） being significantly higher than in sunny conditions （9.7%）. Through interpretability analysis， the following findings are revealed： ①Weather conditions significantly modulate the influencing factors of traffic conflicts； ②Under sunny conditions， spatial vehicle positions （particularly whether the conflicted vehicle is located in a ramp or weaving lane） dominate the severity of conflicts； ③In rainy conditions， the intensity of influencing factors increases markedly， and traffic flow characteristics emerge as the predominant factors. These findings provide a theoretical foundation for differentiated management and safety prevention and control in weaving areas.

Key words: transportation planning and management, traffic conflict, weather impact, model interpretability, time to collision