Prediction of pedestrian dynamics in complex architectures with artificial neural networks

References

• Alahi, A., Goel, K., Ramanathan, V., Robicquet, A., Fei-Fei, L., & Savarese, S. (2016). Social LSTM: Human trajectory prediction in crowded spaces. Paper presented at the IEEE ICCV Conference, Las Vegas, NV, USA (pp. 961–971).
   Google Scholar
• Bando, M., Hasebe, K., Nakayama, A., Shibata, A., & Sugiyama, Y. (1995). Dynamical model of traffic congestion and numerical simulation. Physical Review E, 51(2), 1035–1042.
   PubMed Web of Science ®Google Scholar
• Burnham, K., & Anderson, D. (2002). Model selection and multimodel inference. New York, NY: Springer.
   Google Scholar
• Chen, Y., Everett, M., Liu, M., & How, J. P. (2017). Socially aware motion planning with deep reinforcement learning. Paper presented at the IEEE IROS Conference (pp. 1343–1350), Vancouver, BC, Canada.
   Google Scholar
• Chraibi, M., Ezaki, T., Tordeux, A., Nishinari, K., Schadschneider, A., & Seyfried, A. (2015). Jamming transitions in force-based models for pedestrian dynamics. Physical Review E, 92, 042809.
   Web of Science ®Google Scholar
• Chraibi, M., Seyfried, A., & Schadschneider, A. (2010). Generalized centrifugal-force model for pedestrian dynamics. Physical Review E, 82(4), 046111.
   Web of Science ®Google Scholar
• Chraibi, M., Tordeux, A., Schadschneider, A., & Seyfried, A. (2018). Modelling of pedestrian and evacuation dynamics (2nd ed.). In R. A. Meyers (Ed.), Encyclopedia of complexity and systems science (pp. 1–22). Berlin: Springer.
   Google Scholar
• Daamen, W. (2004). Modelling passenger flows in public transport facilities (Dissertation), TU Delft, Delft, The Netherlands.
   Google Scholar
• Das, P., Parida, M., & Katiyar, V. K. (2015). Analysis of interrelationship between pedestrian flow parameters using artificial neural network. Journal of Modern Transportation, 23(4), 298–309. doi:10.1007/s40534-015-0088-9
   Web of Science ®Google Scholar
• Duives, D. C., Daamen, W., & Hoogendoorn, S. P. (2013). State-of-the-art crowd motion simulation models. Transportation Research Part C: Emerging Technologies, 37, 193–209. doi:10.1016/j.trc.2013.02.005
   Web of Science ®Google Scholar
• Forschungszentrum Jülich. (2018a). Bottleneck experiment. Retrieved from http://ped.fz-juelich.de/da/2009unidirClosed
   Google Scholar
• Forschungszentrum Jülich. (2018b). Corridor experiment. Retrieved from http://ped.fz-juelich.de/da/2009bottleneck
   Google Scholar
• Forschungszentrum Jülich. (2018c). Dataset of experimental pedestrian trajectories. Retrieved from http://ped.fz-juelich.de/database
   Google Scholar
• Forschungszentrum Jülich. (2018d). Dokumentation von Versuchen zur Personenstromdynamik – Projekt “HERMES”. Retrieved from http://ped.fz-juelich.de/experiments/2009.05.12_Duesseldorf_Messe_Hermes/docu/VersuchsdokumentationHERMES.pdf
   Google Scholar
• Fragkiadaki, K., Levine, S., Felsen, P., & Malik, J. (2015). Recurrent network models for human dynamics. Paper presented at the IEEE ICCV Conference (pp. 4346–4354), Santiago, Chile.
   Google Scholar
• Fritsch, S., Guenther, F., & Suling, M. (2012). Neuralnet: Training of neural networks [Computer software manual]. Retrieved from http://CRAN.R-project.org/package=neuralnet
   Google Scholar
• Greenberg, H. (1959). An analysis of traffic flow. Operations Research, 7(1), 79–85. doi:10.1287/opre.7.1.79
   Web of Science ®Google Scholar
• Greenshields, B. (1935). A study of traffic capacity. Paper presented at the Highway Research Board Proceedings (Vol. 14, pp. 448–477), Washington, DC.
   Google Scholar
• Guo, R., Wong, S., Huang, H., Zhang, P., & Lam, W. (2010). A microscopic pedestrian-simulation model and its application to intersecting flows. Physica A, 389(3), 515–526. doi:10.1016/j.physa.2009.10.008
   Web of Science ®Google Scholar
• Helbing, D., Buzna, L., Johansson, A., & Werner, T. (2005). Self-organized pedestrian crowd dynamics: Experiments, simulations, and design solutions. Transportation Science, 39(1), 1–24. doi:10.1287/trsc.1040.0108
   Web of Science ®Google Scholar
• Helbing, D., & Molnár, P. (1995). Social force model for pedestrian dynamics. Physical Review E, 51(5), 4282–4286.
   PubMed Web of Science ®Google Scholar
• Holl, S., Schadschneider, A., & Seyfried, A. (2014). Hermes: An evacuation assistant for large arenas. In U. Weidmann, U. Kirsch, & M. Schreckenberg (Eds.), Pedestrian and evacuation dynamics 2012 (pp. 345–349). Cham: Springer International Publishing.
   Google Scholar
• Jackel, L., Hackett, D., Krotkov, E., Perschbacher, M., Pippine, J., & Sullivan, C. (2007). How DARPA structures its robotics programs to improve locomotion and navigation. Communications of the ACM, 50(11), 55–59. doi:10.1145/1297797.1297823
   Web of Science ®Google Scholar
• Kohavi, R. (1995). A study of cross-validation and bootstrap for accuracy estimation and model selection. In C. S. Mellish (Ed.), Proceedings of the 14th international joint conference on artificial intelligence (Vol. 2, pp. 1137–1143). San Francisco, CA: Morgan Kaufmann Publishers Inc.
   Google Scholar
• Li, Y., Khoshelham, K., Sarvi, M., & Haghani, M. (2019). Direct generation of level of service maps from images using convolutional and long short-term memory networks. Journal of Intelligent Transportation Systems, 23(3), 300–308. doi:10.1080/15472450.2018.1563865
   Web of Science ®Google Scholar
• Liao, W., Seyfried, A., Zhang, J., Boltes, M., Zheng, X., & Zhao, Y. (2014). Experimental study on pedestrian flow through wide bottleneck. Transportation Research Procedia, 2, 26–33. doi:10.1016/j.trpro.2014.09.005
   Google Scholar
• Lv, W., Song, W.-g., Ma, J., & Fang, Z.-m. (2013). A two-dimensional optimal velocity model for unidirectional pedestrian flow based on pedestrian’s visual hindrance field. IEEE Transactions on Intelligent Transportation Systems, 14(4), 1753–1763. doi:10.1109/TITS.2013.2266340
   Web of Science ®Google Scholar
• Ma, Y., Lee, E. W. M., & Yuen, R. K. K. (2016). An artificial intelligence-based approach for simulating pedestrian movement. IEEE Transactions on Intelligent Transportation Systems, 17(11), 3159–3170. doi:10.1109/TITS.2016.2542843
   Web of Science ®Google Scholar
• Mooney, C., & Duval, R. (1993). Bootstrapping: A nonparametric approach to statistical inference. Thousand Oaks, CA: SAGE Publications.
   Google Scholar
• Moussaïd, M., Guillot, E., Moreau, M., Fehrenbach, J., Chabiron, O., Lemercier, S., … Theraulaz, G. (2012). Traffic instabilities in self-organized pedestrian crowds. PLoS Computational Biology, 8(3), 1–10.
   PubMed Web of Science ®Google Scholar
• Nakayama, A., Hasebe, K., & Sugiyama, Y. (2005). Instability of pedestrian flow and phase structure in a two-dimensional optimal velocity model. Physical Review E, 71, 036121.
   Web of Science ®Google Scholar
• Parisi, D., & Patterson, G. (2017). Influence of bottleneck lengths and position on simulated pedestrian egress. Papers in Physics, 9, 090001.
   Web of Science ®Google Scholar
• Predtechenskii, V. M., & Milinskii, A. I. (1978). Planning for foot traffic flow in buildings. New-Dehli, India: Amerind.
   Google Scholar
• R Core Team. (2014). R: A language and environment for statistical computing [Computer software manual]. Retrieved from http://www.R-project.org/
   Google Scholar
• Rumelhart, D., Hinton, G., & Williams, R. (1986). Learning representations by back-propagating errors. Nature, 323, 533–536. doi:10.1038/323533a0
   Web of Science ®Google Scholar
• Sadati, N., & Taheri, J. (2002). Solving robot motion planning problem using Hopfield neural network in a fuzzified environment. Paper presented at the IEEE FS Conference (Vol. 2, pp. 1144–1149), Honolulu, HI.
   Google Scholar
• Schadschneider, A., Chraibi, M., Seyfried, A., Tordeux, A., & Zhan, J. (2018). Pedestrian dynamics - From empirical results to modeling. In L. Gibelli & N. Bellomo (Eds.), Crowd dynamics, volume 1. Modeling and simulation in science, engineering and technology. Cham: Birkhäuser.
   Google Scholar
• Schadschneider, A., Klingsch, W., Klüpfel, H., Kretz, T., Rogsch, C., & Seyfried, A. (2009). Evacuation dynamics: Empirical results, modeling and applications. In R. A. Meyers (Ed.), Encyclopedia of complexity and systems science (pp. 3142–3176). New York, NY: Springer.
   Google Scholar
• Seyfried, A., Passon, O., Steffen, B., Boltes, M., Rupprecht, T., & Klingsch, W. (2009). New insights into pedestrian flow through bottlenecks. Transportation Science, 43(3), 395–406. doi:10.1287/trsc.1090.0263
   Web of Science ®Google Scholar
• Shladover, S. E. (2018). Connected and automated vehicle systems: Introduction and overview. Journal of Intelligent Transportation Systems, 22(3), 190–200. doi:10.1080/15472450.2017.1336053
   Web of Science ®Google Scholar
• Tordeux, A., Chraibi, M., Seyfried, A., & Schadschneider, A. (2017). Data from: Prediction of pedestrian speed with artificial neural networks. Retrieved from https://doi.org/10.5281/zenodo.1054017
   Google Scholar
• Treiber, M., & Kesting, A. (2013). Traffic flow dynamics. Berlin: Springer.
   Google Scholar
• Weidmann, U. (1994). Transporttechnik der Fußgänger (Technical Report). ETH Zürich: Schriftenreihe des IVT Nr. 90.
   Google Scholar
• Zhang, J. (2012). Pedestrian fundamental diagrams: Comparative analysis of experiments in different geometries (Doctoral dissertation), Universität Wuppertal, Wuppertal. Retrieved from http://juser.fz-juelich.de/record/128157
   Google Scholar
• Zhang, J., & Seyfried, A. (2014). Experimental studies of pedestrian flows under different boundary conditions. Paper presented at the ITSC IEEE Conference (pp. 542–547), Qingdao, China.
   Google Scholar
• Zhang, Y., Xin, D.-R., & Wu, Y.-H. (2016). Pedestrian detection for traffic safety based on accumulate binary haar features and improved deep belief network algorithm. Transportation Planning and Technology, 39(8), 791–800.
   Web of Science ®Google Scholar