Enhancing Discrete Choice Models with Representation Learning

Abstract

In discrete choice modeling (DCM), model misspecifications may lead to limited predictability and biased parameter estimates. In this paper, we propose a new approach for estimating choice models in which we divide the systematic part of the utility specification into (i) a knowledge-driven part, and (ii) a data-driven one, which learns a new representation from available explanatory variables. Our formulation increases the predictive power of standard DCM without sacrificing their interpretability. We show the effectiveness of our formulation by augmenting the utility specification of the Multinomial Logit (MNL) and the Nested Logit (NL) models with a new non-linear representation arising from a Neural Network (NN), leading to new choice models referred to as the Learning Multinomial Logit (L-MNL) and Learning Nested Logit (L-NL) models. Using multiple publicly available datasets based on revealed and stated preferences, we show that our models outperform the traditional ones, both in terms of predictive performance and accuracy in parameter estimation. All source code of the models are shared to promote open science.

arXiv comment35 pages, 12 tables, 6 figures, +11 p. Appendix

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• Crowd-Robot Interaction: Crowd-aware Robot Navigation with Attention-based Deep Reinforcement Learning
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• RUMBoost: Gradient Boosted Random Utility Models
• Discrete Choice Analysis with Machine Learning Capabilities
• A tactical time slot management problem under mixed logit demand
• Graph neural networks for residential location choice: connection to classical logit models
• Fully Data-driven but Interpretable Human Behavioural Modelling with Differentiable Discrete Choice Model