IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v16y2024i17p7502-d1467199.html
   My bibliography  Save this article

Simulation Optimization of Station-Level Control of Large-Scale Passenger Flow Based on Queueing Network and Surrogate Model

Author

Listed:
  • Wei Wang

    (Department of Automation, Tsinghua University, Beijing 100084, China
    Traffic Control Technology Co., Ltd., Beijing 100071, China)

  • Yindong Ji

    (Department of Automation, Tsinghua University, Beijing 100084, China)

  • Zhonghao Zhao

    (School of Systems Science, Beijing Jiaotong University, Beijing 100044, China)

  • Haodong Yin

    (School of Systems Science, Beijing Jiaotong University, Beijing 100044, China)

Abstract

Urban rail transit encounters supply–demand contradictions during peak hours, seriously affecting passenger experience. Therefore, it is necessary to explore and optimize passenger-flow control strategies for urban rail transit stations during peak hours. However, current research mostly focuses on passenger-flow control at the network level, and there is insufficient exploration of specific operational strategies at the station level. At the same time, the microscopic simulation model for passenger-flow control at the station level faces the challenge of balancing efficiency and accuracy. This paper presents a simulation optimization approach to optimize the station-level passenger-flow controlling measures, based on a queueing network and surrogate model, aiming to improve throughput, minimize congestion, and enhance passenger experience. The first stage of the method modeled the urban railway station using queueing network theory and multi-agent theory, and then built a mesoscale simulation model that was based on an urban railway station. In the second stage, a passenger flow management and control model for ingress flow was established by combining the Kriging model with a queuing network model, and the particle swarm optimization algorithm was used to solve the model. On this basis, a simulation optimization method for station passenger-flow control was established. Finally, we conducted an example analysis of Zhongguancun Station on the Beijing subway. By comparing the simulation results before and after control, as well as comparing the optimal control scheme obtained by this method with the results of other control schemes, the results showed that the simulation optimization method proposed in this paper can propose an optimal passenger-flow control scheme. By using this method, stations can significantly enhance sustainability. For example, the method not only saves human resources but also effectively avoids or reduces congestion, boosting passenger travel efficiency and safety. By minimizing wait times, these methods lower energy consumption and support the sustainable development of public transportation systems, contributing to more sustainable urban environments.

Suggested Citation

  • Wei Wang & Yindong Ji & Zhonghao Zhao & Haodong Yin, 2024. "Simulation Optimization of Station-Level Control of Large-Scale Passenger Flow Based on Queueing Network and Surrogate Model," Sustainability, MDPI, vol. 16(17), pages 1-34, August.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:17:p:7502-:d:1467199
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/16/17/7502/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/16/17/7502/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Carolina Osorio & Michel Bierlaire, 2013. "A Simulation-Based Optimization Framework for Urban Transportation Problems," Operations Research, INFORMS, vol. 61(6), pages 1333-1345, December.
    2. Gipps, P.G. & Marksjö, B., 1985. "A micro-simulation model for pedestrian flows," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 27(2), pages 95-105.
    3. Carolina Osorio & Linsen Chong, 2015. "A Computationally Efficient Simulation-Based Optimization Algorithm for Large-Scale Urban Transportation Problems," Transportation Science, INFORMS, vol. 49(3), pages 623-636, August.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Subramani Palani Niranjan & Suthanthira Raj Devi Latha & Sorin Vlase, 2024. "Cost Optimization in Sintering Process on the Basis of Bulk Queueing System with Diverse Services Modes and Vacation," Mathematics, MDPI, vol. 12(22), pages 1-19, November.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Huo, Jinbiao & Liu, Chengqi & Chen, Jingxu & Meng, Qiang & Wang, Jian & Liu, Zhiyuan, 2023. "Simulation-based dynamic origin–destination matrix estimation on freeways: A Bayesian optimization approach," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 173(C).
    2. Flötteröd, Gunnar, 2017. "A search acceleration method for optimization problems with transport simulation constraints," Transportation Research Part B: Methodological, Elsevier, vol. 98(C), pages 239-260.
    3. Osorio, Carolina, 2019. "High-dimensional offline origin-destination (OD) demand calibration for stochastic traffic simulators of large-scale road networks," Transportation Research Part B: Methodological, Elsevier, vol. 124(C), pages 18-43.
    4. Carolina Osorio & Krishna Kumar Selvam, 2017. "Simulation-Based Optimization: Achieving Computational Efficiency Through the Use of Multiple Simulators," Transportation Science, INFORMS, vol. 51(2), pages 395-411, May.
    5. Osorio, Carolina & Wang, Carter, 2017. "On the analytical approximation of joint aggregate queue-length distributions for traffic networks: A stationary finite capacity Markovian network approach," Transportation Research Part B: Methodological, Elsevier, vol. 95(C), pages 305-339.
    6. Zhou, Tianli & Fields, Evan & Osorio, Carolina, 2023. "A data-driven discrete simulation-based optimization algorithm for car-sharing service design," Transportation Research Part B: Methodological, Elsevier, vol. 178(C).
    7. David Schmaranzer & Roland Braune & Karl F. Doerner, 2021. "Multi-objective simulation optimization for complex urban mass rapid transit systems," Annals of Operations Research, Springer, vol. 305(1), pages 449-486, October.
    8. Hyoshin (John) Park & Ali Haghani & Song Gao & Michael A. Knodler & Siby Samuel, 2018. "Anticipatory Dynamic Traffic Sensor Location Problems with Connected Vehicle Technologies," Service Science, INFORMS, vol. 52(6), pages 1299-1326, December.
    9. Carolina Osorio & Jana Yamani, 2017. "Analytical and Scalable Analysis of Transient Tandem Markovian Finite Capacity Queueing Networks," Transportation Science, INFORMS, vol. 51(3), pages 823-840, August.
    10. Carolina Osorio & Kanchana Nanduri, 2015. "Energy-Efficient Urban Traffic Management: A Microscopic Simulation-Based Approach," Transportation Science, INFORMS, vol. 49(3), pages 637-651, August.
    11. Xiqun (Michael) Chen & Xiang He & Chenfeng Xiong & Zheng Zhu & Lei Zhang, 2019. "A Bayesian Stochastic Kriging Optimization Model Dealing with Heteroscedastic Simulation Noise for Freeway Traffic Management," Transportation Science, INFORMS, vol. 53(2), pages 545-565, March.
    12. Zhang, Chao & Osorio, Carolina & Flötteröd, Gunnar, 2017. "Efficient calibration techniques for large-scale traffic simulators," Transportation Research Part B: Methodological, Elsevier, vol. 97(C), pages 214-239.
    13. Linsen Chong & Carolina Osorio, 2018. "A Simulation-Based Optimization Algorithm for Dynamic Large-Scale Urban Transportation Problems," Transportation Science, INFORMS, vol. 52(3), pages 637-656, June.
    14. Tay, Timothy & Osorio, Carolina, 2022. "Bayesian optimization techniques for high-dimensional simulation-based transportation problems," Transportation Research Part B: Methodological, Elsevier, vol. 164(C), pages 210-243.
    15. Xiao Chen & Carolina Osorio & Bruno Filipe Santos, 2019. "Simulation-Based Travel Time Reliable Signal Control," Transportation Science, INFORMS, vol. 53(2), pages 523-544, March.
    16. Xiang He & Xiqun (Michael) Chen & Chenfeng Xiong & Zheng Zhu & Lei Zhang, 2017. "Optimal Time-Varying Pricing for Toll Roads Under Multiple Objectives: A Simulation-Based Optimization Approach," Transportation Science, INFORMS, vol. 51(2), pages 412-426, May.
    17. David Schmaranzer & Roland Braune & Karl F. Doerner, 2020. "Population-based simulation optimization for urban mass rapid transit networks," Flexible Services and Manufacturing Journal, Springer, vol. 32(4), pages 767-805, December.
    18. Zheng, Liang & Xue, Xinfeng & Xu, Chengcheng & Ran, Bin, 2019. "A stochastic simulation-based optimization method for equitable and efficient network-wide signal timing under uncertainties," Transportation Research Part B: Methodological, Elsevier, vol. 122(C), pages 287-308.
    19. Osorio, Carolina & Punzo, Vincenzo, 2019. "Efficient calibration of microscopic car-following models for large-scale stochastic network simulators," Transportation Research Part B: Methodological, Elsevier, vol. 119(C), pages 156-173.
    20. Ezaki, Takahiro & Yanagisawa, Daichi & Ohtsuka, Kazumichi & Nishinari, Katsuhiro, 2012. "Simulation of space acquisition process of pedestrians using Proxemic Floor Field Model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(1), pages 291-299.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:16:y:2024:i:17:p:7502-:d:1467199. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.