Choice modelling in the age of machine learning - Discussion paper

van Cranenburgh, S.; Wang, Shenhao; Vij, Akshay; Pereira, Francisco; Walker, Joan

doi:10.1016/j.jocm.2021.100340

Choice modelling in the age of machine learning - Discussion paper

Title

Choice modelling in the age of machine learning - Discussion paper

Author

van Cranenburgh, S. (TU Delft Transport and Logistics)
Wang, Shenhao (Massachusetts Institute of Technology)
Vij, Akshay (University of South Australia)
Pereira, Francisco (Technical University of Denmark)
Walker, Joan (University of California)

Date

2022

Abstract

Since its inception, the choice modelling field has been dominated by theory-driven modelling approaches. Machine learning offers an alternative data-driven approach for modelling choice behaviour and is increasingly drawing interest in our field. Cross-pollination of machine learning models, techniques and practices could help overcome problems and limitations encountered in the current theory-driven modelling paradigm, such as subjective labour-intensive search processes for model selection, and the inability to work with text and image data. However, despite the potential benefits of using the advances of machine learning to improve choice modelling practices, the choice modelling field has been hesitant to embrace machine learning. This discussion paper aims to consolidate knowledge on the use of machine learning models, techniques and practices for choice modelling, and discuss their potential. Thereby, we hope not only to make the case that further integration of machine learning in choice modelling is beneficial, but also to further facilitate it. To this end, we clarify the similarities and differences between the two modelling paradigms; we review the use of machine learning for choice modelling; and we explore areas of opportunities for embracing machine learning models and techniques to improve our practices. To conclude this discussion paper, we put forward a set of research questions which must be addressed to better understand if and how machine learning can benefit choice modelling.

Subject

Choice modelling
Literature overview
Machine learning
Research agenda

To reference this document use:

http://resolver.tudelft.nl/uuid:e7636b15-974c-4268-9d8c-918f07b8105d

DOI

https://doi.org/10.1016/j.jocm.2021.100340

ISSN

1755-5345

Source

Journal of Choice Modelling, 42

Part of collection

Institutional Repository