A Probabilistic Framework for Estimating the Risk of Pedestrian-Vehicle Conflicts at Intersections

• Published in: IEEE transactions on intelligent transportation systems 2023-12, Vol.24 (12), p.14111-14120
• Main Authors: Li, Pei, Guo, Huizhong, Bao, Shan, Kusari, Arpan
• Format: Article
• Language: English
• Subjects: Accidents; Crashes; Gaussian process; Maneuvers; Mathematical models; Pedestrian safety; Pedestrians; proactive traffic safety management; Regression models; Risk; Roads; Safety; Safety management; Safety measures; Sensitivity; Statistical analysis; surrogate safety measures; traffic conflicts; Traffic intersections; Traffic management; Trajectory; Turning; Vehicles
• ISSN: 1524-9050; 1558-0016
• DOI: 10.1109/TITS.2023.3296567

Pedestrian safety has become a critical issue due to the increase in pedestrian crashes every year, while proactive traffic safety management based on surrogate safety measures (SSMs) has been considered one of the key approaches to improving pedestrian safety. However, existing SSMs are developed based on the assumption that road users will maintain constant speed and direction. Risk estimations based on this assumption are less stable and more likely to be exaggerated. Considering the limitations of existing SSMs, this study has proposed a probabilistic framework for estimating the risk of pedestrian-vehicle conflicts at intersections. The proposed framework works by predicting the trajectories of vehicles and pedestrians using Gaussian process regression models and incorporating these results with the probability of vehicles making different maneuvers. The proposed framework has been evaluated using both simulated and real-world data collected at an intersection. The simulation results validated an increased estimated risk given time-critical pedestrian-vehicle conflicts, as well as a higher probability of the vehicle maneuver that led to such conflicts. This observation remained even when multiple conflicts arose from different directions. Moreover, experimental results using real-world data suggested that the proposed framework outperformed traditional time-to-collision (TTC) in terms of conflict prediction, quantification, and localization. For example, the proposed framework had a sensitivity of 0.92 in terms of conflict prediction, while TTC had a sensitivity of 0.62. Furthermore, the proposed framework required much less computation time compared to deep learning methods, which made it an optimal choice for proactive pedestrian safety solutions at intersections.