Comparing and contrasting choice model and machine learning techniques in the context of vehicle ownership decisions

Azam Ali, Arash Kalatian and Charisma F. Choudhury

Transportation Research Part A: Policy and Practice, 2023, vol. 173, issue C

Abstract: In recent years, planners have started considering Machine Learning (ML) techniques as an alternative to discrete choice models (CM). ML techniques are primarily data-driven and typically achieve better prediction accuracy compared to CM. However, it is hypothesized that since the ML techniques do not have the strong grounding to economic theory as the CMs, they may not perform well in contexts that are radically different from the ‘training’ scenario. It is also hypothesized that the relative prediction performance may be affected by the metrics used for comparing the models.

Keywords: Choice modelling; Machine learning; Explainable machine learning; Vehicle ownership model; Developing countries; Car ownership (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1016/j.tra.2023.103727

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