IDEAS home Printed from https://ideas.repec.org/a/spr/eurjtl/v8y2019i5d10.1007_s13676-019-00142-9.html
   My bibliography  Save this article

Solving stochastic frequency-based assignment to transit networks with pre-trip/en-route path choice

Author

Listed:
  • Massimo Gangi

    (Università degli Studi di Messina)

  • Giulio E. Cantarella

    (Università degli Studi di Salerno)

  • Antonino Vitetta

    (Università Mediterranea degli Studi di Reggio Calabria)

Abstract

This paper deals with the stochastic frequency-based assignment for transit systems, considering pre-trip/en-route path choice behaviour; this problem is relevant for (uncongested or congested) urban transit networks, where travelers may not completely know the status of service, say bus arrivals at stops, when they leave the origin; under mild conditions, travel strategy can be modelled by hyperpaths. Hyperpath choice behaviour can be described through random utility models thus properly modelling several unavoidable sources of uncertainty, which cannot be considered by the commonly used deterministic choice model. Effective methods suitable for large scale applications are proposed for solving stochastic assignment based on probit or gammit choice models, which properly model the effects of hyperpath overlapping, even though their application requires Montecarlo techniques; Montecarlo techniques based on Sobol numbers are compared with those based on the commonly used Mersenne Twister ones; several MSA-based algorithms for equilibrium assignment are discussed and compared with the commonly used basic implementation. Applications to a toy and a large scale network is also discussed.

Suggested Citation

  • Massimo Gangi & Giulio E. Cantarella & Antonino Vitetta, 2019. "Solving stochastic frequency-based assignment to transit networks with pre-trip/en-route path choice," EURO Journal on Transportation and Logistics, Springer;EURO - The Association of European Operational Research Societies, vol. 8(5), pages 661-681, December.
  • Handle: RePEc:spr:eurjtl:v:8:y:2019:i:5:d:10.1007_s13676-019-00142-9
    DOI: 10.1007/s13676-019-00142-9
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s13676-019-00142-9
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s13676-019-00142-9?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Warren B. Powell & Yosef Sheffi, 1982. "The Convergence of Equilibrium Algorithms with Predetermined Step Sizes," Transportation Science, INFORMS, vol. 16(1), pages 45-55, February.
    2. Jia Hao Wu & Michael Florian & Patrice Marcotte, 1994. "Transit Equilibrium Assignment: A Model and Solution Algorithms," Transportation Science, INFORMS, vol. 28(3), pages 193-203, August.
    3. Ennio Cascetta, 2009. "Transportation Systems Analysis," Springer Optimization and Its Applications, Springer, number 978-0-387-75857-2, June.
    4. Cepeda, M. & Cominetti, R. & Florian, M., 2006. "A frequency-based assignment model for congested transit networks with strict capacity constraints: characterization and computation of equilibria," Transportation Research Part B: Methodological, Elsevier, vol. 40(6), pages 437-459, July.
    5. Giulio Erberto Cantarella, 1997. "A General Fixed-Point Approach to Multimode Multi-User Equilibrium Assignment with Elastic Demand," Transportation Science, INFORMS, vol. 31(2), pages 107-128, May.
    6. Roberto Cominetti & José Correa, 2001. "Common-Lines and Passenger Assignment in Congested Transit Networks," Transportation Science, INFORMS, vol. 35(3), pages 250-267, August.
    7. Nguyen, S. & Pallottino, S., 1988. "Equilibrium traffic assignment for large scale transit networks," European Journal of Operational Research, Elsevier, vol. 37(2), pages 176-186, November.
    8. Wichmann, B.A. & Hill, I.D., 2006. "Generating good pseudo-random numbers," Computational Statistics & Data Analysis, Elsevier, vol. 51(3), pages 1614-1622, December.
    9. Carlos F. Daganzo, 1983. "Stochastic Network Equilibrium with Multiple Vehicle Types and Asymmetric, Indefinite Link Cost Jacobians," Transportation Science, INFORMS, vol. 17(3), pages 282-300, August.
    10. Henry Liu & Xiaozheng He & Bingsheng He, 2009. "Method of Successive Weighted Averages (MSWA) and Self-Regulated Averaging Schemes for Solving Stochastic User Equilibrium Problem," Networks and Spatial Economics, Springer, vol. 9(4), pages 485-503, December.
    11. Carlos F. Daganzo & Yosef Sheffi, 1977. "On Stochastic Models of Traffic Assignment," Transportation Science, INFORMS, vol. 11(3), pages 253-274, August.
    12. Marchi, A. & Liverani, A. & Del Giudice, A., 2009. "Polynomial pseudo-random number generator via cyclic phase," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 79(11), pages 3328-3338.
    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. Yin, Jiateng & D’Ariano, Andrea & Wang, Yihui & Yang, Lixing & Tang, Tao, 2021. "Timetable coordination in a rail transit network with time-dependent passenger demand," European Journal of Operational Research, Elsevier, vol. 295(1), pages 183-202.

    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. Sun, S. & Szeto, W.Y., 2018. "Logit-based transit assignment: Approach-based formulation and paradox revisit," Transportation Research Part B: Methodological, Elsevier, vol. 112(C), pages 191-215.
    2. Li, Guoyuan & Chen, Anthony, 2023. "Strategy-based transit stochastic user equilibrium model with capacity and number-of-transfers constraints," European Journal of Operational Research, Elsevier, vol. 305(1), pages 164-183.
    3. Du, Muqing & Chen, Anthony, 2022. "Sensitivity analysis for transit equilibrium assignment and applications to uncertainty analysis," Transportation Research Part B: Methodological, Elsevier, vol. 157(C), pages 175-202.
    4. Nielsen, Otto Anker, 2000. "A stochastic transit assignment model considering differences in passengers utility functions," Transportation Research Part B: Methodological, Elsevier, vol. 34(5), pages 377-402, June.
    5. Jiang, Y. & Szeto, W.Y., 2016. "Reliability-based stochastic transit assignment: Formulations and capacity paradox," Transportation Research Part B: Methodological, Elsevier, vol. 93(PA), pages 181-206.
    6. Fan, Yinchao & Ding, Jianxun & Liu, Haoxiang & Wang, Yu & Long, Jiancheng, 2022. "Large-scale multimodal transportation network models and algorithms-Part I: The combined mode split and traffic assignment problem," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 164(C).
    7. Fan, Yinchao & Ding, Jianxun & Long, Jiancheng & Wu, Jianjun, 2024. "Modeling and evaluating the travel behaviour in multimodal networks: A path-based unified equilibrium model and a tailored greedy solution algorithm," Transportation Research Part A: Policy and Practice, Elsevier, vol. 182(C).
    8. 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).
    9. Guido Gentile, 2018. "New Formulations of the Stochastic User Equilibrium with Logit Route Choice as an Extension of the Deterministic Model," Service Science, INFORMS, vol. 52(6), pages 1531-1547, December.
    10. 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.
    11. 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.
    12. 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.
    13. Hamdouch, Younes & Szeto, W.Y. & Jiang, Y., 2014. "A new schedule-based transit assignment model with travel strategies and supply uncertainties," Transportation Research Part B: Methodological, Elsevier, vol. 67(C), pages 35-67.
    14. Ma, Jie & Meng, Qiang & Cheng, Lin & Liu, Zhiyuan, 2022. "General stochastic ridesharing user equilibrium problem with elastic demand," Transportation Research Part B: Methodological, Elsevier, vol. 162(C), pages 162-194.
    15. G. E. Cantarella & D. P. Watling, 2016. "Modelling road traffic assignment as a day-to-day dynamic, deterministic process: a unified approach to discrete- and continuous-time models," EURO Journal on Transportation and Logistics, Springer;EURO - The Association of European Operational Research Societies, vol. 5(1), pages 69-98, March.
    16. Paolo Delle Site, 2017. "On the Equivalence Between SUE and Fixed-Point States of Day-to-Day Assignment Processes with Serially-Correlated Route Choice," Networks and Spatial Economics, Springer, vol. 17(3), pages 935-962, September.
    17. Shang, Pan & Li, Ruimin & Guo, Jifu & Xian, Kai & Zhou, Xuesong, 2019. "Integrating Lagrangian and Eulerian observations for passenger flow state estimation in an urban rail transit network: A space-time-state hyper network-based assignment approach," Transportation Research Part B: Methodological, Elsevier, vol. 121(C), pages 135-167.
    18. Wang, Zhichao & Jiang, Rui & Jiang, Yu & Gao, Ziyou & Liu, Ronghui, 2024. "Modelling bus bunching along a common line corridor considering passenger arrival time and transfer choice under stochastic travel time," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 181(C).
    19. Ren, Hualing & Song, Yingjie & Long, Jiancheng & Si, Bingfeng, 2021. "A new transit assignment model based on line and node strategies," Transportation Research Part B: Methodological, Elsevier, vol. 150(C), pages 121-142.
    20. Kenetsu Uchida & Agachai Sumalee & David Watling & Richard Connors, 2007. "A Study on Network Design Problems for Multi-modal Networks by Probit-based Stochastic User Equilibrium," Networks and Spatial Economics, Springer, vol. 7(3), pages 213-240, September.

    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:spr:eurjtl:v:8:y:2019:i:5:d:10.1007_s13676-019-00142-9. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.