Traffic Conflict

Traffic conflict analysis aims to understand and predict risky interactions between road users to improve traffic safety. Current research focuses on developing robust and generalizable methods for detecting conflicts from various data sources (e.g., video, GPS trajectories), often employing machine learning techniques like statistical learning and graph neural networks to analyze complex interactions and predict accident likelihood. These advancements are crucial for developing more effective collision warning systems, improving infrastructure design, and gaining a deeper understanding of driver behavior in diverse traffic scenarios. The availability of large, publicly accessible datasets is also a key area of ongoing development.