Predicting Pedestrian Crossing Behavior at Urban Intersections Using Machine Learning Techniques : Evidence from Hyderabad, India

Open Access

Issue		EPJ Web Conf. Volume 379, 2026 2^nd International Conference on Sustainable Materials, Methodologies, Technologies & Applications in Engineering (ICS2MT-2026)


Article Number		04002
Number of page(s)		13
Section		Transportation Engineering, Smart Mobility and Sustainable Infrastructure
DOI		https://doi.org/10.1051/epjconf/202637904002
Published online		03 August 2026

M.S. Al Hasanat, A.B. Hassanat, O. Alharasees, et al., Interpretable machine learning for imbalanced pedestrian injury severity prediction in urban Jordan. J. King Saud Univ. Eng. Sci. 37, 39 (2025). https://doi.org/10.1007/s44444-025-00053-3 [Google Scholar]
J. Cai, M. Wang, Y. Wu, Research on pedestrian crossing decision models and predictions based on machine learning. Sensors 24(1), 258 (2024). https://doi.org/10.3390/s24010258 [Google Scholar]
A.M. Darwish, S. Shokry, M. Zagow, M. Elbany, A. Qabur, T.O. Alshammari, M.S. Alfiqi, Investigating unsafe pedestrian behavior at urban road midblock crossings using machine learning: Lessons from Alexandria, Egypt. Buildings 16(3), 505 (2026). https://doi.org/10.3390/buildings16030505 [Google Scholar]
X. Gu, C. Li, L. Gao, X. Niu, A review of pedestrian trajectory prediction methods based on deep learning technology. Sensors 25(23), 7360 (2025). https://doi.org/10.3390/s25237360 [Google Scholar]
M. Haghani, Empirical methods in pedestrian, crowd and evacuation dynamics: Part I. Experimental methods and emerging topics. Saf. Sci. 129, 104743 (2020). https://doi.org/10.1016/j.ssci.2020.104743 [Google Scholar]
A. Hossain, X. Sun, R. Thapa, J. Codjoe, Applying association rules mining to investigate pedestrian fatal and injury crash patterns under different lighting conditions. Transp. Res. Rec. 2676(6), 659–672 (2022). https://doi.org/10.1177/03611981221076120 [Google Scholar]
S. Ul Arifeen, M. Ali, E. Macioszek, Analysis of vehicle–pedestrian crash severity using advanced machine learning techniques. Arch. Transp. 68(4), 91–116 (2023). https://doi.org/10.61089/aot2023.ttb8p367 [Google Scholar]
M.N. Khan, S. Das, J. Liu, Predicting pedestrian-involved crash severity using Inception-V3 deep learning model. Accid. Anal. Prev. 197, 107457 (2024). https://doi.org/10.1016/j.aap.2024.107457 [Google Scholar]
S. Dai, J. Liu, N.-M. Cheung, Uncertainty-aware pedestrian crossing prediction via reinforcement learning. IEEE Trans. Circuits Syst. Video Technol. 34(10), 9540–9549 (2024). https://doi.org/10.1109/TCSVT.2024.3400391 [Google Scholar]
A. Mirzabagheri, M. Ahmadi, N. Zhang, R. Alirezaee, S. Mozaffari, S. Alirezaee, Navigating uncertainty: Advanced techniques in pedestrian intention prediction for autonomous vehicles—A comprehensive review. Vehicles 7(2), 57 (2025). https://doi.org/10.3390/vehicles7020057 [Google Scholar]
Z. Liu, Y. Xue, L. Zhu, Pedestrian crossing intention prediction in obstructed scenarios using graph-based feature reconstruction and multi-scale self-attentive encoding. Eng. Appl. Artif. Intell. 159, 111748 (2025). https://doi.org/10.1016/j.engappai.2025.111748 [Google Scholar]
H. Razali, T. Mordan, A. Alahi, Pedestrian intention prediction: A convolutional bottom-up multi-task approach. Transp. Res. Part C Emerg. Technol. 130, 103259 (2021). https://doi.org/10.1016/j.trc.2021.103259 [Google Scholar]
A. Kalatian, B. Farooq, A context-aware pedestrian trajectory prediction framework for automated vehicles. Transp. Res. Part C Emerg. Technol. 134, 103453 (2022). https://doi.org/10.1016/j.trc.2021.103453 [Google Scholar]
W. Tao, M. Aghaabbasi, M. Ali, A.H. Almaliki, R. Zainol, A.A. Almaliki, E.E. Hussein, An advanced machine learning approach to predicting pedestrian fatality caused by road crashes: A step toward sustainable pedestrian safety. Sustainability 14(4), 2436 (2022). https://doi.org/10.3390/su14042436 [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.