IDEAS home Printed from https://ideas.repec.org/a/spr/pubtra/v16y2024i3d10.1007_s12469-024-00363-w.html
   My bibliography  Save this article

A systematic review on crowding valuation in public transport

Author

Listed:
  • Rupam Fedujwar

    (Indian Institute of Technology Roorkee)

  • Amit Agarwal

    (Indian Institute of Technology Roorkee)

Abstract

In public transport, crowding is one of the variables that is likely to influence the decisions of the choice makers. Crowding has become a subject of concern in metropolitan areas, triggering passenger travel behavior, such as shifting from public to private modes of transport, changing routes or departure times, etc. Hence, there is a need to understand the effect of crowding in public transport and its influence on the behavior of travelers. Therefore, this review investigates essential factors (e.g., crowding representation, crowding measurement, modeling framework, etc.) after reviewing the 40 screened studies on the valuation of crowding in public transport. The paper’s findings show that the passenger perception towards crowding is different for varying levels of crowding, modes of transport, study areas, data types, different modeling frameworks, and the underlying distribution of the attribute parameters. A meta-analysis is performed to show the influence of explanatory variables affecting the value of the time multiplier. A net-salary-based city classification is used to make the results transferable. Lastly, this work provides a direction for the selection of the crowding representation, measure, and valuation for future studies. Further, several research gaps are identified for the model formulation, valuation, crowding at different locations, non-linearity, etc.

Suggested Citation

  • Rupam Fedujwar & Amit Agarwal, 2024. "A systematic review on crowding valuation in public transport," Public Transport, Springer, vol. 16(3), pages 743-773, October.
    • Handle: RePEc:spr:pubtra:v:16:y:2024:i:3:d:10.1007_s12469-024-00363-w
    DOI: 10.1007/s12469-024-00363-w
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s12469-024-00363-w
    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/s12469-024-00363-w?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. 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.
    2. Tirachini, Alejandro & Hensher, David A. & Rose, John M., 2014. "Multimodal pricing and optimal design of urban public transport: The interplay between traffic congestion and bus crowding," Transportation Research Part B: Methodological, Elsevier, vol. 61(C), pages 33-54.
    3. Sanmay Shelat & Thijs van de Wiel & Eric Molin & J W C van Lint & Oded Cats, 2022. "Analysing the impact of COVID-19 risk perceptions on route choice behaviour in train networks," PLOS ONE, Public Library of Science, vol. 17(3), pages 1-19, March.
    4. David Moher & Alessandro Liberati & Jennifer Tetzlaff & Douglas G Altman & The PRISMA Group, 2009. "Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA Statement," PLOS Medicine, Public Library of Science, vol. 6(7), pages 1-6, July.
    5. Dilay Çelebi & Şükrü İmre, 2020. "Measuring crowding-related comfort in public transport," Transportation Planning and Technology, Taylor & Francis Journals, vol. 43(7), pages 735-750, October.
    6. Li, Zheng & Hensher, David A., 2011. "Crowding and public transport: A review of willingness to pay evidence and its relevance in project appraisal," Transport Policy, Elsevier, vol. 18(6), pages 880-887, November.
    7. Tang, Yili & Jiang, Yu & Yang, Hai & Nielsen, Otto Anker, 2020. "Modeling and optimizing a fare incentive strategy to manage queuing and crowding in mass transit systems," Transportation Research Part B: Methodological, Elsevier, vol. 138(C), pages 247-267.
    8. Feifei Qin, 2014. "Investigating the In-Vehicle Crowding Cost Functions for Public Transit Modes," Mathematical Problems in Engineering, Hindawi, vol. 2014, pages 1-13, February.
    9. Hörcher, Daniel & Graham, Daniel J. & Anderson, Richard J., 2017. "Crowding cost estimation with large scale smart card and vehicle location data," Transportation Research Part B: Methodological, Elsevier, vol. 95(C), pages 105-125.
    10. David A. Hensher & John M. Rose & Waiyan Leong & Alejandro Tirachini & Zheng Li, 2013. "Choosing Public Transport—Incorporating Richer Behavioural Elements in Modal Choice Models," Transport Reviews, Taylor & Francis Journals, vol. 33(1), pages 92-106, January.
    11. Mark Wardman & Gerard Whelan, 2011. "Twenty Years of Rail Crowding Valuation Studies: Evidence and Lessons from British Experience," Transport Reviews, Taylor & Francis Journals, vol. 31(3), pages 379-398.
    12. Bansal, Prateek & Hurtubia, Ricardo & Tirachini, Alejandro & Daziano, Ricardo A., 2019. "Flexible estimates of heterogeneity in crowding valuation in the New York City subway," Journal of choice modelling, Elsevier, vol. 31(C), pages 124-140.
    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. Luan, Xiaojie & Corman, Francesco, 2022. "Passenger-oriented traffic control for rail networks: An optimization model considering crowding effects on passenger choices and train operations," Transportation Research Part B: Methodological, Elsevier, vol. 158(C), pages 239-272.
    2. Rossetti, Tomás & Daziano, Ricardo A., 2024. "Crowding multipliers on shared transportation in New York City: The effects of COVID-19 and implications for a sustainable future," Transport Policy, Elsevier, vol. 145(C), pages 224-236.
    3. Chen, Xin & Jiang, Yu & Bláfoss Ingvardson, Jesper & Luo, Xia & Anker Nielsen, Otto, 2023. "I can board, but I’d rather wait: Active boarding delay choice behaviour analysis using smart card data in metro systems," Transportation Research Part A: Policy and Practice, Elsevier, vol. 174(C).
    4. Hörcher, Daniel & Tirachini, Alejandro, 2021. "A review of public transport economics," Economics of Transportation, Elsevier, vol. 25(C).
    5. Bouscasse, Hélène & de Lapparent, Matthieu, 2019. "Perceived comfort and values of travel time savings in the Rhône-Alpes Region," Transportation Research Part A: Policy and Practice, Elsevier, vol. 124(C), pages 370-387.
    6. Hörcher, Daniel & Graham, Daniel J., 2018. "Demand imbalances and multi-period public transport supply," Transportation Research Part B: Methodological, Elsevier, vol. 108(C), pages 106-126.
    7. Yap, Menno & Cats, Oded, 2021. "Taking the path less travelled: Valuation of denied boarding in crowded public transport systems," Transportation Research Part A: Policy and Practice, Elsevier, vol. 147(C), pages 1-13.
    8. Batarce, Marco & Muñoz, Juan Carlos & Ortúzar, Juan de Dios, 2016. "Valuing crowding in public transport: Implications for cost-benefit analysis," Transportation Research Part A: Policy and Practice, Elsevier, vol. 91(C), pages 358-378.
    9. Junya Kumagai & Mihoko Wakamatsu & Shunsuke Managi, 2021. "Do commuters adapt to in-vehicle crowding on trains?," Transportation, Springer, vol. 48(5), pages 2357-2399, October.
    10. Shelat, Sanmay & Cats, Oded & van Cranenburgh, Sander, 2022. "Traveller behaviour in public transport in the early stages of the COVID-19 pandemic in the Netherlands," Transportation Research Part A: Policy and Practice, Elsevier, vol. 159(C), pages 357-371.
    11. Prateek Bansal & Roselinde Kessels & Rico Krueger & Daniel J Graham, 2021. "Face masks, vaccination rates and low crowding drive the demand for the London Underground during the COVID-19 pandemic," Papers 2107.02394, arXiv.org.
    12. Tirachini, Alejandro & Hurtubia, Ricardo & Dekker, Thijs & Daziano, Ricardo A., 2017. "Estimation of crowding discomfort in public transport: Results from Santiago de Chile," Transportation Research Part A: Policy and Practice, Elsevier, vol. 103(C), pages 311-326.
    13. Haywood, Luke & Koning, Martin, 2015. "The distribution of crowding costs in public transport: New evidence from Paris," Transportation Research Part A: Policy and Practice, Elsevier, vol. 77(C), pages 182-201.
    14. Singh, Jyotsna & Homem de Almeida Correia, Gonçalo & van Wee, Bert & Barbour, Natalia, 2023. "Change in departure time for a train trip to avoid crowding during the COVID-19 pandemic: A latent class study in the Netherlands," Transportation Research Part A: Policy and Practice, Elsevier, vol. 170(C).
    15. Svanberg , Lisa & Pyddoke, Roger, 2020. "Policies for on-board crowding in public transportation : a literature review," Working Papers 2020:6, Swedish National Road & Transport Research Institute (VTI).
    16. Bansal, Prateek & Kessels, Roselinde & Krueger, Rico & Graham, Daniel J., 2022. "Preferences for using the London Underground during the COVID-19 pandemic," Transportation Research Part A: Policy and Practice, Elsevier, vol. 160(C), pages 45-60.
    17. Peftitsi, Soumela & Jenelius, Erik & Cats, Oded, 2022. "Modeling the effect of real-time crowding information (RTCI) on passenger distribution in trains," Transportation Research Part A: Policy and Practice, Elsevier, vol. 166(C), pages 354-368.
    18. Soza-Parra, Jaime & Raveau, Sebastián & Muñoz, Juan Carlos & Cats, Oded, 2019. "The underlying effect of public transport reliability on users’ satisfaction," Transportation Research Part A: Policy and Practice, Elsevier, vol. 126(C), pages 83-93.
    19. Jenelius, Erik, 2018. "Public transport experienced service reliability: Integrating travel time and travel conditions," Transportation Research Part A: Policy and Practice, Elsevier, vol. 117(C), pages 275-291.
    20. Bansal, Prateek & Hurtubia, Ricardo & Tirachini, Alejandro & Daziano, Ricardo A., 2019. "Flexible estimates of heterogeneity in crowding valuation in the New York City subway," Journal of choice modelling, Elsevier, vol. 31(C), pages 124-140.

    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:pubtra:v:16:y:2024:i:3:d:10.1007_s12469-024-00363-w. 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.