aceEfficient video-based traffic conflict prediction and interpretable risk analysis at signalized intersections via deep learning
Objectives To achieve accurate and real-time prediction of traffic conflicts at signalized intersections and identify their key contributing factors, thereby supporting proactive safety management and reducing accident risks. Methods This study proposes a novel multi-stage traffic-conflict prediction framework that integrates a real-time video image processing system and an advanced conflict-prediction model. Specifically, a real-time video analysis system integrating the YOLOv8 object detection framework and th…
A multi-stage framework integrating YOLOv8 detection, OC-SORT tracking, dynamic CROI filtering, and ST-GAT prediction enables accurate real-time traffic-conflict prediction at signalized intersections to enhance safety and mitigate accident risks.
Evaluation is limited to field video data from a single representative signalized intersection in Nanning, China, which constrains generalizability to other intersection designs, traffic mixes, and environmental conditions.
Evidence
- Peer-reviewedTraffic Injury Prevention2026-08-13
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Truvace Impact Record TRV-2026-0767, v1: “Efficient video-based traffic conflict prediction and interpretable risk analysis at signalized intersections via deep learning.” Truvace, 2026-08-15. /record/TRV-2026-0767 (accessed at citation time). sha256 661a88c277ec56fa…
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