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

Tim Hillel, Michel Bierlaire, Mohammed Z.E.B. Elshafie and Ying Jin

Journal of choice modelling, 2021, vol. 38, issue C

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.

Keywords: Choice modelling; Machine learning; Classification; Discrete choice models; Neural networks; Systematic review (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (13)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:eejocm:v:38:y:2021:i:c:s1755534520300208

DOI: 10.1016/j.jocm.2020.100221

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