A multi-agent-based approach for the impacts analysis of passenger flow on platforms in metro stations considering train operations
Abstract
Impacts analysis of train operation on passenger flow in metro stations is an important and fundamental requirement to improve the operational efficiency and ensure passengers a high level of service. This study aims at large metro stations where thousands of passengers are moving, boarding or alighting and the complicated interactions among passengers and between passengers and other entities like stairways or trains take place all the time. A multi-agent-based approach is developed from the investigation of movement characteristics of passengers to meet the above requirement and deal with such interactions. The simulation scenarios considering the various conditions of train operations are performed in the case studies of a metro station in Beijing (China) to prove the feasibility of the proposed approach, which is useful to formulate and evaluate the operation schemes of trains.
Keyword : metro station, passenger flow, train operation, multi-agent-based approach
How to Cite
Chen, S., Zhang, Y., Di, Y., Li, F., & Jia, W. (2018). A multi-agent-based approach for the impacts analysis of passenger flow on platforms in metro stations considering train operations. Transport, 33(3), 821-834. https://doi.org/10.3846/transport.2018.5663
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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Chen, S.; Zhou, R.; Zhou, Y.; Mao, B. 2013. Computation on bus delay at stops in Beijing through statistical analysis, Mathematical Problems in Engineering 2013: 1–9. https://doi.org/10.1155/2013/745370
Fang, Z.; Lo, S. M.; Lu, J. A. 2003. On the relationship between crowd density and movement velocity, Fire Safety Journal 38(3): 271–283. https://doi.org/10.1016/S0379-7112(02)00058-9
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GB 50157-2013. Code for Design of Metro. China National Standard. Standardization Administration of the People’s Republic of China (in Chinese).
Guo, R.-Y. 2014. New insights into discretization effects in cellular automata models for pedestrian evacuation, Physica A: Statistical Mechanics and its Applications 400: 1–11. https://doi.org/10.1016/j.physa.2014.01.001
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. https://doi.org/10.1287/trsc.1040.0108
Helbing, D.; Johansson, A.; Mathiesen, J.; Jensen, M. H.; Hansen, A. 2006. Analytical approach to continuous and intermittent bottleneck flows, Physical Review Letters 97(16): 1–4. https://doi.org/10.1103/PhysRevLett.97.168001
Helbing, D.; Molnár, P. 1995. Social force model for pedestrian dynamics, Physical Review E 51(5): 4282–4286. https://doi.org/10.1103/PhysRevE.51.4282
Huo, F.; Song, W.; Lv, W.; Liew, K. M. 2014. Analyzing pedestrian merging flow on a floor–stair interface using an extended lattice gas model, Simulation: Transactions of the Society for Modeling and Simulation International 90(5): 501–510. https://doi.org/10.1177/0037549714526294
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Jiang, Y.; Hu, L.; Zhu, J.; Chen, Y. 2013. PH fitting of the arrival interval distribution of the passenger flow on urban rail transit stations, Applied Mathematics and Computation 225: 158–170. https://doi.org/10.1016/j.amc.2013.09.005
Kosonen, I. 2003. Multi-agent fuzzy signal control based on real-time simulation, Transportation Research Part C: Emerging Technologies 11(5): 389–403. https://doi.org/10.1016/S0968-090X(03)00032-9
Lam, W. H. K.; Cheung, C.-Y.; Lam, C. F. 1999. A study of crowding effects at the Hong Kong light rail transit stations, Transportation Research Part A: Policy and Practice 33(5): 401–415. https://doi.org/10.1016/S0965-8564(98)00050-0
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Miyoshi, T.; Nakayasu, H.; Ueno, Y.; Patterson, P. 2012. An emergency aircraft evacuation simulation considering passenger emotions, Computers & Industrial Engineering 62(3): 746–754. https://doi.org/10.1016/j.cie.2011.11.012
National Academies of Sciences, Engineering, and Medicine. 2013. Transit Capacity and Quality of Service Manual. 3rd edition. Washington, DC, US: The National Academies Press. 685 p. https://doi.org/10.17226/24766
Niazi, M.; Hussain, A. 2011. Agent-based computing from multi-agent systems to agent-based models: a visual survey, Scientometrics 89(2): 479–499. https://doi.org/10.1007/s11192-011-0468-9
Niu, H.; Zhou, X. 2013. Optimizing urban rail timetable under time-dependent demand and oversaturated conditions, Transportation Research Part C: Emerging Technologies 36: 212–230. https://doi.org/10.1016/j.trc.2013.08.016
Qu, L.; Chow, W. K. 2012. Platform screen doors on emergency evacuation in underground railway stations, Tunnelling and Underground Space Technology 30: 1–9. https://doi.org/10.1016/j.tust.2011.09.003
Ren, G.; Zhao, X.; Li, Y. 2014. Route optimization model for pedestrian evacuation in metro hubs, Journal of Central South University 21(2): 822–831. https://doi.org/10.1007/s11771-014-2006-4
Shi, C.; Zhong, M.; Nong, X.; He, L.; Shi, J.; Feng, G. 2012. Modeling and safety strategy of passenger evacuation in a metro station in China, Safety Science 50(5): 1319–1332. https://doi.org/10.1016/j.ssci.2010.07.017
Wan, J.; Sui, J.; Yu, H. 2014. Research on evacuation in the subway station in China based on the combined social force model, Physica A: Statistical Mechanics and its Applications 394: 33–46. https://doi.org/10.1016/j.physa.2013.09.060
Wong, R. C. W.; Yuen, T. W. Y.; Fung, K. W.; Leung, J. M. Y. 2008. Optimizing timetable synchronization for rail mass transit, Transportation Science 42(1): 57–69. https://doi.org/10.1287/trsc.1070.0200
Wu, G.-Y.; Chien, S.-W.; Huang, Y.-T. 2010. Modeling the occupant evacuation of the mass rapid transit station using the control volume model, Building and Environment 45(10): 2280–2288. https://doi.org/10.1016/j.buildenv.2010.04.015
Zhang, X.; Li, X.; Hadjisophocleous, G. 2013. A probabilistic occupant evacuation model for fire emergencies using Monte Carlo methods, Fire Safety Journal 58: 15–24. https://doi.org/10.1016/j.firesaf.2013.01.028
Zhang, J.; Seyfried, A. 2014. Comparison of intersecting pedestrian flows based on experiments, Physica A: Statistical Mechanics and its Applications 405: 316–325. https://doi.org/10.1016/j.physa.2014.03.004
Zhong, M.; Shi, C.; Tu, X.; Fu, T.; He, L. 2008. Study of the human evacuation simulation of metro fire safety analysis in China, Journal of Loss Prevention in the Process Industries 21(3): 287–298. https://doi.org/10.1016/j.jlp.2007.08.001