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

Author

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  • 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
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    Citations

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    Cited by:

    1. 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).
    2. 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.
    3. 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).
    4. 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.
    5. 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.
    6. 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).
    7. 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.
    8. 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).
    9. 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.

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