IDEAS home Printed from https://ideas.repec.org/a/eee/eejocm/v38y2021ics1755534520300208.html
   My bibliography  Save this article

A systematic review of machine learning classification methodologies for modelling passenger mode choice

Author

Listed:
  • Hillel, Tim
  • Bierlaire, Michel
  • Elshafie, Mohammed Z.E.B.
  • Jin, Ying

Abstract

Machine Learning (ML) approaches are increasingly being investigated as an alternative to Random Utility Models (RUMs) for modelling passenger mode choice. These approaches have the potential to provide valuable insights into choice modelling research questions. However, the research and the methodologies used are fragmented. Whilst systematic reviews on RUMs for mode choice prediction have long existed and the methods have been well scrutinised for mode choice prediction, the same is not true for ML models. To address this need, this paper conducts a systematic review of ML methodologies for modelling passenger mode choice. The review analyses the methodologies employed within each study to (a) establish the state-of-research frameworks for ML mode choice modelling and (b) identify and quantify the prevalence of methodological limitations in previous studies.

Suggested Citation

  • Hillel, Tim & Bierlaire, Michel & Elshafie, Mohammed Z.E.B. & Jin, Ying, 2021. "A systematic review of machine learning classification methodologies for modelling passenger mode choice," Journal of choice modelling, Elsevier, vol. 38(C).
    • Handle: RePEc:eee:eejocm:v:38:y:2021:i:c:s1755534520300208
    DOI: 10.1016/j.jocm.2020.100221
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1755534520300208
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.jocm.2020.100221?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.

    Citations

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


    Cited by:

    1. Liu, Jianing & Wen, Xiao & Jian, Sisi, 2024. "Toward better equity: Analyzing travel patterns through a neural network approach in mobility-as-a-service," Transport Policy, Elsevier, vol. 153(C), pages 110-126.
    2. Smeele, Nicholas V.R. & Chorus, Caspar G. & Schermer, Maartje H.N. & de Bekker-Grob, Esther W., 2023. "Towards machine learning for moral choice analysis in health economics: A literature review and research agenda," Social Science & Medicine, Elsevier, vol. 326(C).
    3. María Vega-Gonzalo & Panayotis Christidis, 2022. "Fair Models for Impartial Policies: Controlling Algorithmic Bias in Transport Behavioural Modelling," Sustainability, MDPI, vol. 14(14), pages 1-23, July.
    4. Ashik, F.R. & Sreezon, A.I.Z. & Rahman, M.H. & Zafri, N.M. & Labib, S.M., 2024. "Built environment influences commute mode choice in a global south megacity context: Insights from explainable machine learning approach," Journal of Transport Geography, Elsevier, vol. 116(C).
    5. Amirreza Talebi, 2024. "Simulation in discrete choice models evaluation: SDCM, a simulation tool for performance evaluation of DCMs," Papers 2407.17014, arXiv.org, revised Jul 2024.
    6. Dubey, Subodh & Cats, Oded & Hoogendoorn, Serge & Bansal, Prateek, 2022. "A multinomial probit model with Choquet integral and attribute cut-offs," Transportation Research Part B: Methodological, Elsevier, vol. 158(C), pages 140-163.
    7. Gutiérrez-Vargas, Álvaro A. & Meulders, Michel & Vandebroek, Martina, 2023. "Modeling preference heterogeneity using model-based decision trees," Journal of choice modelling, Elsevier, vol. 46(C).
    8. Qingyi Wang & Shenhao Wang & Yunhan Zheng & Hongzhou Lin & Xiaohu Zhang & Jinhua Zhao & Joan Walker, 2023. "Deep hybrid model with satellite imagery: how to combine demand modeling and computer vision for behavior analysis?," Papers 2303.04204, arXiv.org, revised Feb 2024.
    9. Wang, Qingyi & Wang, Shenhao & Zheng, Yunhan & Lin, Hongzhou & Zhang, Xiaohu & Zhao, Jinhua & Walker, Joan, 2024. "Deep hybrid model with satellite imagery: How to combine demand modeling and computer vision for travel behavior analysis?," Transportation Research Part B: Methodological, Elsevier, vol. 179(C).
    10. S. Van Cranenburgh & S. Wang & A. Vij & F. Pereira & J. Walker, 2021. "Choice modelling in the age of machine learning -- discussion paper," Papers 2101.11948, arXiv.org, revised Nov 2021.

    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:eee:eejocm:v:38:y:2021:i:c:s1755534520300208. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/journal-of-choice-modelling .

    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.