Modeling pedestrians’ near-accident events at signalized intersections using gated recurrent unit (GRU)

Allen, 1978 Archer, 2004 Borsos, 2020, Are collision and crossing course surrogate safety indicators transferable? A probability based approach using extreme value theory, Accid. Anal. Prev., 143, 10.1016/j.aap.2020.105517 Bradski, 2019 Centers for Disease Control and Prevention, 2019 Chawla, 2002, Smote: synthetic minority over-sampling technique, J. Artif. Int. Res., 16, 321 Cho, 2014, Learning phrase representations using rnn encoder-decoder for statistical machine translation, arXiv preprint arXiv, 1406, 1078 Coles, 2001 Cooper, 1984, Experience with traffic conflicts in canada with emphasis on “ post encroachment time” techniques. NATO Advance Study Inst Ser F, J. Comput. Syst. Sci. Int., 5 Farah, 2017, Safety analysis of passing maneuvers using extreme value theory, Iatss Res., 41, 12, 10.1016/j.iatssr.2016.07.001 Fu, 2020, Multivariate bayesian hierarchical modeling of the non-stationary traffic conflict extremes for crash estimation, Anal. Methods Accid. Res. Gilleland, 2016, Extremes 2.0: an extreme value analysis package in r, J. Stat. Softw., 72, 1, 10.18637/jss.v072.i08 Gong, 2020, Multi-objective reinforcement learning approach for improving safety at intersections with adaptive traffic signal control, Accid. Anal. Prev., 144, 10.1016/j.aap.2020.105655 Governors Highway Safety Association, 2020 Graves, A., Mohamed, A.-R., Hinton, G., Year. Speech recognition with deep recurrent neural networks. In: Proceedings of the 2013 IEEE international conference on acoustics, speech and signal processing, pp. 6645-6649. Hayward, 1972, 384 He, K., Gkioxari, G., Dollár, P., Girshick, R., Year. Mask r-cnn. In: Proceedings of the 2017 IEEE International Conference on Computer Vision (ICCV), pp. 2980-2988. Hochreiter, 1997, Long short-term memory, Neural Comput., 9, 1735, 10.1162/neco.1997.9.8.1735 Hupfer, 1997 Hydén, 1987, 70 Hydén, 1984, 133 Ismail, 2009, Automated analysis of pedestrian-vehicle conflicts using video data, Transp. Res. Rec., 2140 Ismail, 2010, Automated analysis of pedestrian–vehicle conflicts: context for before-and-after studies, Transp. Res. Rec., 2198, 52, 10.3141/2198-07 Kathuria, 2020, Evaluating pedestrian vehicle interaction dynamics at un-signalized intersections: a proactive approach for safety analysis, Accid. Anal. Prev., 134, 10.1016/j.aap.2019.105316 Kingma, 2014, Adam: a method for stochastic optimization, arXiv preprint arXiv, 1412, 6980 Leadbetter, 2012 Li, 2020, Real-time crash risk prediction on arterials based on lstm-cnn, Accid. Anal. Prev., 135, 10.1016/j.aap.2019.105371 Mahmud, 2017, Application of proximal surrogate indicators for safety evaluation: a review of recent developments and research needs, IATSS Res., 41, 153, 10.1016/j.iatssr.2017.02.001 Mohamed, 2013, Motion prediction methods for surrogate safety analysis, Transp. Res. Rec., 2386, 168, 10.3141/2386-19 Naphade, 2019, The 2019 ai city challenge, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, 452 Ni, 2016, Evaluation of pedestrian safety at intersections: a theoretical framework based on pedestrian-vehicle interaction patterns, Accid. Anal. Prev., 96, 118, 10.1016/j.aap.2016.07.030 Orsini, 2019, Collision prediction in roundabouts: a comparative study of extreme value theory approaches, Transp. A Transp. Sci., 15, 556 Ou zheng, 2019 Ou Zheng, 2019 Ou Zheng, 2019 Pascanu, R., Mikolov, T., Bengio, Y., Year. On the difficulty of training recurrent neural networks, pp. 1310-1318. Ren, S., He, K., Girshick, R., Sun, J., Year. Faster r-cnn: Towards real-time object detection with region proposal networks. In: Proceedings of the Advances in neural information processing systems, pp. 91-99. Schuster, 1997, Bidirectional recurrent neural networks, IEEE Trans. Signal Process., 45, 2673, 10.1109/78.650093 Shelby, S.G., Year. Delta-v as a measure of traffic conflict severity. In: Proceedings of the 3rd International Conference on Road Safety and Simulati. September, pp. 14-16. Smith, 1985, Maximum likelihood estimation in a class of nonregular cases, Biometrika, 72, 67, 10.1093/biomet/72.1.67 Songchitruksa, 2006, The extreme value theory approach to safety estimation, Accid. Anal. Prev., 38, 811, 10.1016/j.aap.2006.02.003 Španhel, 2019, Vehicle re-identification and multi-camera tracking in challenging city-scale environment, Proc. CVPR Workshops Tang, 2019, Cityflow: a city-scale benchmark for multi-target multi-camera vehicle tracking and re-identification, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 8797 Tarko, 2012, Use of crash surrogates and exceedance statistics to estimate road safety, Accid. Anal. Prev., 45, 230, 10.1016/j.aap.2011.07.008 Tarko, 2018, Estimating the expected number of crashes with traffic conflicts and the lomax distribution – a theoretical and numerical exploration, Accid. Anal. Prev., 113, 63, 10.1016/j.aap.2018.01.008 Tarko, 2019 Tarko, 2009 Tarko, A.P., Songchitruksa, P., Year. Estimating the frequency of crashes as extreme traffic events. In: Proceedings of the 84th Annual Meeting of the Transportation Research Board. Team, 2013 Wang, 2018, A combined use of microscopic traffic simulation and extreme value methods for traffic safety evaluation, Transp. Res. Part C Emerg. Technol., 90, 281, 10.1016/j.trc.2018.03.011 Wang, 2019, A crash prediction method based on bivariate extreme value theory and video-based vehicle trajectory data, Accid. Anal. Prev., 123, 365, 10.1016/j.aap.2018.12.013 Wang, 2019, Enhancing transportation systems via deep learning: a survey, Transp. Res. Part C Emerg. Technol., 99, 144, 10.1016/j.trc.2018.12.004 Wu, 2020, Automated safety diagnosis based on unmanned aerial vehicle video and deep learning algorithm, Transp. Res. Rec., 10.1177/0361198120925808 Yue, 2020, In-depth approach for identifying crash causation patterns and its implications for pedestrian crash prevention, J. Safety Res., 73, 119, 10.1016/j.jsr.2020.02.020 Zaki, 2014, Automated analysis of pedestrians’ nonconforming behavior and data collection at an urban crossing, Transp. Res. Rec., 2443, 123, 10.3141/2443-14 Zhang, 2020, Prediction of pedestrian crossing intentions at intersections based on long short-term memory recurrent neural network, Transp. Res. Rec. Zhao, 2018, Travel time prediction: based on gated recurrent unit method and data fusion, IEEE Access, 6, 70463, 10.1109/ACCESS.2018.2878799 Zhao, 2019, T-gcn: a temporal graph convolutional network for traffic prediction, IEEE Trans. Intell. Transp. Syst., 1, 10.1109/TITS.2019.2914795 Zheng, 2019, Comparison of traffic conflict indicators for crash estimation using peak over threshold approach, Transp. Res. Rec., 2673, 493, 10.1177/0361198119841556 Zheng, 2019, From univariate to bivariate extreme value models: approaches to integrate traffic conflict indicators for crash estimation, Transp. Res. Part C Emerg. Technol., 103, 211, 10.1016/j.trc.2019.04.015 Zheng, 2019, A full bayes approach for traffic conflict-based before–after safety evaluation using extreme value theory, Accid. Anal. Prev., 131, 308, 10.1016/j.aap.2019.07.014 Zheng, 2018, Bivariate extreme value modeling for road safety estimation, Accid. Anal. Prev., 120, 83, 10.1016/j.aap.2018.08.004