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Transit Users’ Route‐Choice Modelling in Transit Assignment: A Review

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  • Yulin Liu
  • Jonathan Bunker
  • Luis Ferreira

Abstract

This paper reviews the main studies on transit users’ route choice in the context of transit assignment. The studies are categorized into three groups: static transit assignment, within‐day dynamic transit assignment, and emerging approaches. The motivations and behavioural assumptions of these approaches are re‐examined. The first group includes shortest‐path heuristics in all‐or‐nothing assignment, random utility maximization route‐choice models in stochastic assignment, and user equilibrium based assignment. The second group covers within‐day dynamics in transit users’ route choice, transit network formulations, and dynamic transit assignment. The third group introduces the emerging studies on behavioural complexities, day‐to‐day dynamics, and real‐time dynamics in transit users’ route choice. Future research directions are also discussed.

Suggested Citation

  • Yulin Liu & Jonathan Bunker & Luis Ferreira, 2010. "Transit Users’ Route‐Choice Modelling in Transit Assignment: A Review," Transport Reviews, Taylor & Francis Journals, vol. 30(6), pages 753-769, March.
    • Handle: RePEc:taf:transr:v:30:y:2010:i:6:p:753-769
    DOI: 10.1080/01441641003744261
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    Citations

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    Cited by:

    1. Hörcher, Daniel & Tirachini, Alejandro, 2021. "A review of public transport economics," Economics of Transportation, Elsevier, vol. 25(C).
    2. Nassir, Neema & Hickman, Mark & Ma, Zhen-Liang, 2019. "A strategy-based recursive path choice model for public transit smart card data," Transportation Research Part B: Methodological, Elsevier, vol. 126(C), pages 528-548.
    3. Canca, David & Andrade-Pineda, José Luis & De los Santos, Alicia & Calle, Marcos, 2018. "The Railway Rapid Transit frequency setting problem with speed-dependent operation costs," Transportation Research Part B: Methodological, Elsevier, vol. 117(PA), pages 494-519.
    4. Suman, Hemant & Larrain, Homero & Muñoz, Juan Carlos, 2021. "The impact of using a naïve approach in the limited-stop bus service design problem," Transportation Research Part A: Policy and Practice, Elsevier, vol. 149(C), pages 45-61.
    5. Herbon, Avi & Hadas, Yuval, 2015. "Determining optimal frequency and vehicle capacity for public transit routes: A generalized newsvendor model," Transportation Research Part B: Methodological, Elsevier, vol. 71(C), pages 85-99.
    6. Ulrik Berggren & Carmelo D’Agostino & Helena Svensson & Karin Brundell-Freij, 2022. "Intrapersonal variability in public transport path choice due to changes in service reliability," Transportation, Springer, vol. 49(6), pages 1517-1547, December.
    7. 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.
    8. Cats, Oded & West, Jens & Eliasson, Jonas, 2016. "A dynamic stochastic model for evaluating congestion and crowding effects in transit systems," Transportation Research Part B: Methodological, Elsevier, vol. 89(C), pages 43-57.
    9. König, Eva & Schön, Cornelia, 2021. "Railway delay management with passenger rerouting considering train capacity constraints," European Journal of Operational Research, Elsevier, vol. 288(2), pages 450-465.
    10. Weiya Chen & Xin Liu & Dingfang Chen & Xin Pan, 2019. "Setting Headways on a Bus Route under Uncertain Conditions," Sustainability, MDPI, vol. 11(10), pages 1-13, May.

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