Real-Time Traffic Conflict Prediction at Intersections: A Novel Approach Integrating Statistical Models and Machine Learning

Abstract

Real-time traffic conflict prediction is crucial for developing proactive safety management strategies and improving overall traffic safety. However, existing studies have failed to fully consider the entire process of traffic conflict generation at both signalized and unsignalized intersections. Given this, this study proposes a real-time three-stage approach integrating statistical and machine learning models developed from three perspectives to reveal the influencing factors, occurrence identification, and quantity prediction of traffic conflicts. The results show that the proposed approach can effectively predict traffic conflicts at signalized and nonsignalized intersections. The findings of this study provide new ideas for proactive safety management in urban road networks.