A neural-embedded discrete choice model: Learning taste representation with strengthened interpretability

Yafei Han, Francisco Camara Pereira, Moshe Ben-Akiva and Christopher Zegras

Transportation Research Part B: Methodological, 2022, vol. 163, issue C, 166-186

Abstract: Discrete choice models (DCMs) require a priori knowledge of the utility functions, especially how tastes vary across individuals. Utility misspecification may lead to biased estimates, inaccurate interpretations and limited predictability. In this paper, we utilize a neural network to learn taste representation. Our formulation consists of two modules: a neural network (TasteNet) that learns taste parameters (e.g., time coefficient) as flexible functions of individual characteristics; and a multinomial logit (MNL) model with utility functions defined with expert knowledge. Taste parameters learned by the neural network are fed into the choice model and link the two modules.

Keywords: Discrete choice models; Neural networks; Taste heterogeneity; Interpretability; Utility specification; Machine learning; Deep learning (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (6)

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DOI: 10.1016/j.trb.2022.07.001

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