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Research Article

Microscopic modeling of cyclists interactions with pedestrians in shared spaces: a Gaussian process inverse reinforcement learning approach

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Pages 828-854 | Received 10 Nov 2020, Accepted 18 Feb 2021, Published online: 20 Mar 2021

References

  • Abbeel, P., and A. Y. Ng. 2004. “Apprenticeship Learning via Inverse Reinforcement Learning.” In In the Twenty-First International Conference on Machine Learning, 1–8. Alberta, Canada: Banff.
  • Alsaleh, R., M. Hussein, and T. Sayed. 2020. “Microscopic Behavioural Analysis of Cyclists and Pedestrians Interactions in Shared Space.” Canadian Journal of Civil Engineering 47 (1): 50–62.
  • Alsaleh, R., and T. Sayed. 2020. “Modeling Pedestrian-Cyclist Interactions in Shared Space Using Inverse Reinforcement Learning.” Transportation Research Part F: Traffic Psychology and Behaviour 70: 37–57.
  • Alsaleh, R., T. Sayed, and M. H. Zaki. 2018. “Assessing the Effect of Pedestrians’ Use of Cell Phones on Their Walking Behavior: A Study Based on Automated Video Analysis.” Transportation Research Record 2672 (35): 46–57.
  • Anvari, B., M. G. Bell, A. Sivakumar, and W. Y. Ochieng. 2015. “Modelling Shared Space Users via Rule-Based Social Force Model.” Transportation Research Part C: Emerging Technologies 51: 83–103.
  • Ayres, G., B. Wilson, and J. LeBlanc. 2004. “Method for Identifying Vehicle Movements for Analysis of Field Operational Test Data.” Transportation Research Record 1886 (1): 92–100.
  • Beitel, D., J. Stipancic, K. Manaugh, and L. Miranda-Moreno. 2018. “Assessing Safety of Shared Space Using Cyclist-Pedestrian Interactions and Automated Video Conflict Analysis.” Transportation Research Part D: Transport and Environment 65: 710–724.
  • Bratko, I., T. Urbancic, and C. Sammut. 1995. “Behavioural Cloning: Phenomena, Results and Problems.” In International Federation of Automatic Control IFAC, 143–149. Berlin, Germany.
  • Brockman, G., et al. 2016. “Openai gym.” arXiv preprint arXiv 1606.01540: 1–4.
  • Dias, C., M. Iryo-Asano, H. Nishiuchi, and T. Todoroki. 2018. “Calibrating a Social Force Based Model for Simulating Personal Mobility Vehicles and Pedestrian Mixed Traffic.” Simulation Modelling Practice and Theory 87: 395–411.
  • Figliozzi, M., N. Wheeler, and C. M. Monsere. 2013. “Methodology for Estimating Bicyclist Acceleration and Speed Distributions at Intersections.” Transportation Research Record 2387 (1): 66–75.
  • Finn, C., P. Christiano, P. Abbeel, and S. Levine. 2016. “A Connection Between Generative Adversarial Networks, Inverse Reinforcement Learning, and Energy-Based Models.” arXiv preprint arXiv: 1611.03852: 1–10.
  • Fujii, H., H. Uchida, and S. Yoshimura. 2017. “Agent-based Simulation Framework for Mixed Traffic of Cars, Pedestrians and Trams.” Transportation Research Part C: Emerging Technologies 85: 234–248.
  • Gavriilidou, A., W. Daamen, Y. Yuan, and S. P. Hoogendoorn. 2019. “Modelling Cyclist Queue Formation Using a two-Layer Framework for Operational Cycling Behaviour.” Transportation Research Part C: Emerging Technologies 105: 468–484.
  • Gorrini, A., L. Crociani, G. Vizzari, and S. Bandini. 2018. “Observation Results on Pedestrian-Vehicle Interactions at non-Signalized Intersections Towards Simulation.” Transportation Research Part F: Traffic Psychology and Behaviour 59: 269–285.
  • Helbing, D., and P. Molnar. 1995. “Social Force Model for Pedestrian Dynamics.” Physical Review E 51 (5): 4282–4286.
  • Huang, L., J. Wu, F. You, Z. Lv, and H. Song. 2016. “Cyclist Social Force Model at Unsignalized Intersections with Heterogeneous Traffic.” IEEE Transactions on Industrial Informatics 13 (2): 782–792.
  • Hussein, M., B. Popescu, T. Sayed, and L. Kim. 2016. “Analysis of Road User Behavior and Safety During New York City’s Summer Streets Program.” Transportation Research Record 2586: 120–130.
  • Hussein, M., and T. Sayed. 2017a. “A Bi-Directional Agent-Based Pedestrian Microscopic Model.” Transportmetrica A: Transport Science 13 (4): 326–355.
  • Hussein, M., and T. Sayed. 2017b. “Validation of an Agent-Based Microscopic Pedestrian Simulation Model at a Scramble Phase Signalized Intersection.” Transportation Research Record 2661: 30–42.
  • Huttenlocher, D. P., G. A. Klanderman, and W. A. Rucklidge. 1993. “Comparing Images Using the Hausdorff Distance.” IEEE Transactions on Pattern Analysis & Machine Intelligence 9: 850–863.
  • Ismail, K., T. Sayed, and N. Saunier. 2010. “Automated Analysis of Pedestrian–Vehicle Conflicts: Context for Before-and-After Studies.” Transportation Research Record 2198 (1): 52–64.
  • Ismail, K., T. Sayed, and N. Saunier. 2013. “A Methodology for Precise Camera Calibration for Data Collection Applications in Urban Traffic Scenes.” Canadian Journal of Civil Engineering 40 (1): 57–67.
  • Jiang, R., B. Jia, and Q. S. Wu. 2004. “Stochastic Multi-Value Cellular Automata Models for Bicycle Flow.” Journal of Physics A: Mathematical and General 37 (6): 2063–2072.
  • Jiang, X., W. Wang, H. Guo, Q. Cheng, and K. Bengler. 2019. “Drivers’ Effective Decelerating Zone in an Urban Vehicle-Pedestrian Conflict Situation: Observational Studies and Analyses.” Transportation Research Part D: Transport and Environment 66: 76–84.
  • Jin, S., X. Qu, C. Xu, D. Ma, and D. Wang. 2015. “An Improved Multi-Value Cellular Automata Model for Heterogeneous Bicycle Traffic Flow.” Physics Letters A 379 (39): 2409–2416.
  • Karndacharuk, A., D. J. Wilson, and R. C. Dunn. 2016. “Qualitative Evaluation Study of Urban Shared Spaces in New Zealand.” Transportation Research Part D: Transport and Environment 42: 119–134.
  • Levine, S., and V. Koltun. 2012. “Continuous Inverse Optimal Control with Locally Optimal Examples.” arXiv preprint arXiv:1206.4617: 1–8.
  • Levine, S., Z. Popovic, and V. Koltun. 2011. “Nonlinear Inverse Reinforcement Learning with Gaussian Processes.” In In the Twenty-Four Advances in Neural Information Processing Systems, 19–27. Spain: Granada.
  • Liu, D. C., and J. Nocedal. 1989. “On the Limited Memory BFGS Method for Large Scale Optimization.” Mathematical Programming 45 (1-3): 503–528.
  • Liu, Q., J. Sun, Y. Tian, and L. Xiong. 2020. “Modeling and Simulation of Overtaking Events by Heterogeneous non-Motorized Vehicles on Shared Roadway Segments.” Simulation Modelling Practice and Theory 103: 1–23.
  • Lucas, B. D., and T. Kanade. 1981. “An Iterative Image Registration Technique with an Application to Stereo Vision.” In International Joint Conference on Artificial Intelligence, 674–679. Vancouver, BC.
  • Luo, Y., B. Jia, J. Liu, W. H. Lam, X. Li, and Z. Gao. 2015. “Modeling the Interactions Between car and Bicycle in Heterogeneous Traffic.” Journal of Advanced Transportation 49 (1): 29–47.
  • Ma, X., and D. Luo. 2016. “Modeling Cyclist Acceleration Process for Bicycle Traffic Simulation Using Naturalistic Data.” Transportation Research Part F: Traffic Psychology and Behaviour 40: 130–144.
  • Michon, J. A. 1985. A Critical View of Driver Behavior Models: What do we Know, What Should we do? Boston, MA: Springer.
  • Mnih, V., et al. 2016 “Asynchronous Methods for Deep Reinforcement Learning.” International Conference on Machine Learning. New York, United States, 2016. 1928–1937.
  • Montufar, J., J. Arango, M. Porter, and S. Nakagawa. 2007. “Pedestrians’ Normal Walking Speed and Speed When Crossing a Street.” Transportation Research Record 2002 (1): 90–97.
  • Nagel, K., and M. Schreckenberg. 1992. “A Cellular Automaton Model for Freeway Traffic.” Journal de Physique I 2 (12): 2221–2229.
  • Ng, A. Y., and S. J. Russell. 2000. “Algorithms for Inverse Reinforcement Learning.” In In the International Conference on Machine Learning, 663–670. CA, USA: Stanford.
  • Papadimitriou, E., G. Yannis, and J. Golias. 2009. “A Critical Assessment of Pedestrian Behaviour Models.” Transportation Research Part F: Traffic Psychology and Behaviour 12 (3): 242–255.
  • Rockafellar, R. T., and R. J. B. Wets. 2009. Variational Analysis. 3rd ed. Berlin: Springer Science & Business Media.
  • Saunier, N., and T. Sayed. 2006. “A Feature-Based Tracking Algorithm for Vehicles in Intersections.” The 3rd IEEE Canadian Conference on Computer and Robot Vision. Quebec, Canada, 2006. 59–59.
  • Savitzky, A., and M. J. Golay. 1964. “Smoothing and Differentiation of Data by Simplified Least Squares Procedures.” Analytical Chemistry 36 (8): 1627–1639.
  • Schönauer, R., M. Stubenschrott, W. Huang, C. Rudloff, and M. Fellendorf. 2012. “Modeling Concepts for Mixed Traffic: Steps Toward a Microscopic Simulation Tool for Shared Space Zones.” Transportation Research Record 2316 (1): 114–121.
  • Sutton, R. S., and A. G. Barto. 1998. Introduction to Reinforcement Learning. 1st ed. London, England: MIT press.
  • Taherifar, N., H. Hamedmoghadam, S. Sree, and M. Saberi. 2019. “A Macroscopic Approach for Calibration and Validation of a Modified Social Force Model for Bidirectional Pedestrian Streams.” Transportmetrica A: Transport Science 15 (2): 1637–1661.
  • Teknomo, K. 2006. “Application of Microscopic Pedestrian Simulation Model.” Transportation Research Part F: Traffic Psychology and Behaviour 9 (1): 15–27.
  • Tierney, L., and J. B. Kadane. 1986. “Accurate Approximations for Posterior Moments and Marginal Densities.” Journal of the American Statistical Association 81 (393): 82–86.
  • Tomasi, C., and T. Kanade. 1991. Detection and Tracking of Point Features. Technical Report. Pennsylvania, USA: Carnegie Mellon University.
  • Triana, C. A., et al. 2019. “Active Streets for Children: The Case of the Bogotá Ciclovía.” PloS one 14 (5): e0207791.
  • Trinh, L. T., K. Sano, and K. Hatoyama. 2020. “Modelling and Simulating Head-on Conflict-Solving Behaviour of Motorcycles Under Heterogeneous Traffic Condition in Developing Countries.” Transportmetrica A: Transport Science. doi:10.1080/23249935.2020.1819911.
  • Wang, W. L., S. M. Lo, S. B. Liu, and H. Kuang. 2014. “Microscopic Modeling of Pedestrian Movement Behavior: Interacting with Visual Attractors in the Environment.” Transportation Research Part C: Emerging Technologies 44: 21–33.
  • Williams, C. K., and C. E. Rasmussen. 2006. Gaussian Processes for Machine Learning. Cambridge, MA: MIT press.
  • Wu, Y., E. Mansimov, R. B. Grosse, S. Liao, and J. Ba. 2017. “Scalable Trust-Region Method for Deep Reinforcement Learning using Kronecker-Factored Approximation.” Advances in Neural Information Processing Systems. Long Beaah, CA, 2017. 5279–5288.
  • Zhang, P., X. Y. Li, H. Y. Deng, Z. Y. Lin, X. N. Zhang, and S. C. Wong. 2020. “Potential Field Cellular Automata Model for Overcrowded Pedestrian Flow.” Transportmetrica A: Transport Science 16 (3): 749–775.
  • Zhao, D., W. Wang, C. Li, Z. Li, P. Fu, and X. Hu. 2013. “Modeling of Passing Events in Mixed Bicycle Traffic with Cellular Automata.” Transportation Research Record 2387 (1): 26–34.
  • Zhou, Z., Y. Zhou, Z. Pu, and Y. Xu. 2019. “Simulation of Pedestrian Behavior During the Flashing Green Signal Using a Modified Social Force Model.” Transportmetrica A: Transport Science 15 (2): 1019–1040.
  • Ziebart, B. D., A. L. Maas, J. A. Bagnell, and A. K. Dey. 2008. “Maximum Entropy Inverse Reinforcement Learning.” In the Twenty-Third AAAI Conference on Artificial Intelligence. Chicago, Illinois: In the Twenty-Third AAAI Conference on Artificial Intelligence, 2008. 1433–1438.

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