IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v12y2020i13p5365-d379577.html
   My bibliography  Save this article

The Depth-First Optimal Strategy Path Generation Algorithm for Passengers in a Metro Network

Author

Listed:
  • Kai Lu

    (School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100404, China
    National Engineering Laboratory for Urban Rail Transit Communication and Operation Control, Beijing 100044, China
    Traffic Control Technology Co., Ltd., Beijing 100070, China)

  • Tao Tang

    (School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100404, China
    National Engineering Laboratory for Urban Rail Transit Communication and Operation Control, Beijing 100044, China)

  • Chunhai Gao

    (Traffic Control Technology Co., Ltd., Beijing 100070, China)

Abstract

Passenger behavior analysis is a key issue in passenger assignment research, in which the path choice is a fundamental component. A highly complex transit network offers multiple paths for each origin–destination (OD) pair and thus resulting in more flexible choices for each passenger. To reflect a passenger’s flexible choice for the transit network, the optimal strategy was proposed by other researchers to determine passenger choice behavior. However, only strategy links have been searched in the optimal strategy algorithm and these links cannot complete the whole path. To determine the paths for each OD pair, this study proposes the depth-first path generation algorithm, in which a strategy node concept is newly defined. The proposed algorithm was applied to the Beijing metro network. The results show that, in comparison to the shortest path and the K-shortest path analysis, the proposed depth-first optimal strategy path generation algorithm better represents the passenger behavior more reliably and flexibly.

Suggested Citation

  • Kai Lu & Tao Tang & Chunhai Gao, 2020. "The Depth-First Optimal Strategy Path Generation Algorithm for Passengers in a Metro Network," Sustainability, MDPI, vol. 12(13), pages 1-16, July.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:13:p:5365-:d:379577
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/12/13/5365/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/12/13/5365/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Derrible, Sybil & Kennedy, Christopher, 2010. "The complexity and robustness of metro networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(17), pages 3678-3691.
    2. Spiess, Heinz & Florian, Michael, 1989. "Optimal strategies: A new assignment model for transit networks," Transportation Research Part B: Methodological, Elsevier, vol. 23(2), pages 83-102, April.
    3. Alireza Khani & Mark Hickman & Hyunsoo Noh, 2015. "Trip-Based Path Algorithms Using the Transit Network Hierarchy," Networks and Spatial Economics, Springer, vol. 15(3), pages 635-653, September.
    4. Fivos Papadimitriou, 2012. "The Algorithmic Complexity of Landscapes," Landscape Research, Taylor & Francis Journals, vol. 37(5), pages 591-611, October.
    Full references (including those not matched with items on IDEAS)

    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. Zhang, Yu & Tang, Jiafu, 2018. "Itinerary planning with time budget for risk-averse travelers," European Journal of Operational Research, Elsevier, vol. 267(1), pages 288-303.
    2. Mohammad Nurul Hassan & Taha Hossein Rashidi & Neema Nassir, 2021. "Consideration of different travel strategies and choice set sizes in transit path choice modelling," Transportation, Springer, vol. 48(2), pages 723-746, April.
    3. Pramesh Kumar & Alireza Khani, 2021. "Adaptive Park-and-ride Choice on Time-dependent Stochastic Multimodal Transportation Network," Networks and Spatial Economics, Springer, vol. 21(4), pages 771-800, December.
    4. Khani, Alireza, 2019. "An online shortest path algorithm for reliable routing in schedule-based transit networks considering transfer failure probability," Transportation Research Part B: Methodological, Elsevier, vol. 126(C), pages 549-564.
    5. S. Mahmassani, Hani & F. Hyland, Michael, 2016. "Gap-based transit assignment algorithm with vehicle capacity constraints: Simulation-based implementation and large-scale applicationAuthor-Name: Verbas, Ömer," Transportation Research Part B: Methodological, Elsevier, vol. 93(PA), pages 1-16.
    6. Tong, C.O. & Wong, S.C., 1998. "A stochastic transit assignment model using a dynamic schedule-based network," Transportation Research Part B: Methodological, Elsevier, vol. 33(2), pages 107-121, April.
    7. Xu, Zhandong & Xie, Jun & Liu, Xiaobo & Nie, Yu (Marco), 2020. "Hyperpath-based algorithms for the transit equilibrium assignment problem," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 143(C).
    8. Ding Luo & Oded Cats & Hans Lint, 2020. "Can passenger flow distribution be estimated solely based on network properties in public transport systems?," Transportation, Springer, vol. 47(6), pages 2757-2776, December.
    9. E. Codina & A. Marín & F. López, 2013. "A model for setting services on auxiliary bus lines under congestion," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 21(1), pages 48-83, April.
    10. Preston, John, 2008. "Competition in transit markets," Research in Transportation Economics, Elsevier, vol. 23(1), pages 75-84, January.
    11. Ziliaskopoulos, Athanasios & Wardell, Whitney, 2000. "An intermodal optimum path algorithm for multimodal networks with dynamic arc travel times and switching delays," European Journal of Operational Research, Elsevier, vol. 125(3), pages 486-502, September.
    12. Wu, Di & Yin, Yafeng & Lawphongpanich, Siriphong, 2011. "Pareto-improving congestion pricing on multimodal transportation networks," European Journal of Operational Research, Elsevier, vol. 210(3), pages 660-669, May.
    13. Codina, Esteve & Rosell, Francisca, 2017. "A heuristic method for a congested capacitated transit assignment model with strategies," Transportation Research Part B: Methodological, Elsevier, vol. 106(C), pages 293-320.
    14. Younes Hamdouch & Siriphong Lawphongpanich, 2010. "Congestion Pricing for Schedule-Based Transit Networks," Transportation Science, INFORMS, vol. 44(3), pages 350-366, August.
    15. Miller-Hooks, Elise & Mahmassani, Hani, 2003. "Path comparisons for a priori and time-adaptive decisions in stochastic, time-varying networks," European Journal of Operational Research, Elsevier, vol. 146(1), pages 67-82, April.
    16. Xueguo Xu & Chen Xu & Wenxin Zhang, 2022. "Research on the Destruction Resistance of Giant Urban Rail Transit Network from the Perspective of Vulnerability," Sustainability, MDPI, vol. 14(12), pages 1-26, June.
    17. Laure Rousset & César Ducruet, 2020. "Disruptions in Spatial Networks: a Comparative Study of Major Shocks Affecting Ports and Shipping Patterns," Networks and Spatial Economics, Springer, vol. 20(2), pages 423-447, June.
    18. Mo, Baichuan & Koutsopoulos, Haris N. & Zhao, Jinhua, 2022. "Inferring passenger responses to urban rail disruptions using smart card data: A probabilistic framework," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 159(C).
    19. Hamdouch, Younes & Lawphongpanich, Siriphong, 2008. "Schedule-based transit assignment model with travel strategies and capacity constraints," Transportation Research Part B: Methodological, Elsevier, vol. 42(7-8), pages 663-684, August.
    20. Nair, Rahul & Miller-Hooks, Elise, 2014. "Equilibrium network design of shared-vehicle systems," European Journal of Operational Research, Elsevier, vol. 235(1), pages 47-61.

    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:12:y:2020:i:13:p:5365-:d:379577. 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.