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

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

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.

Suggested Citation

  • Han, Yafei & Pereira, Francisco Camara & Ben-Akiva, Moshe & Zegras, Christopher, 2022. "A neural-embedded discrete choice model: Learning taste representation with strengthened interpretability," Transportation Research Part B: Methodological, Elsevier, vol. 163(C), pages 166-186.
    • Handle: RePEc:eee:transb:v:163:y:2022:i:c:p:166-186
    DOI: 10.1016/j.trb.2022.07.001
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