Let Me Not Lie: Learning MultiNomial Logit

Discrete choice models generally assume that model specification is known a priori. In practice, determiningthe utility specification for a particular application remains a difficult task and model misspecification may lead to biased parameter estimates. In this paper, we propose anew mathematical framework for estimating choice models in which the systematic part of the utility specification is divided into an interpretable part and a learning representation partthat aims at automatically discovering a good utility specification from available data. We show the effectiveness of our framework by augmenting the utility specification of the Multinomial Logit Model (MNL) with a new nonlinear representation arising from a Neural Network (NN). This leads to a new choice model referred to as the Learning Multinomial Logit(L-MNL) model. Our experiments show that our L-MNL model outperformed the traditional MNL models and existing hybrid neural network models both in terms of predictive performance and accuracy in parameter estimation. Keywords: Discrete choice models, Neural networks, Utility specification 1. Introduction Discrete Choice Models (DCM) have emerged as a powerful theoretical framework for analyzing individual travel behavior. The goal of these models is to predict the choice among a given set of discrete alternatives (e.g., choice of walking as transportation mode rather than taking the car or the bus), while understanding the behavioral process that led to the specific choice. For many years, the Multinomial Logit Model (MNL) based on a linear utility specification has provided the foundation for the analysis of discrete choice. Despite its oversimplified assumptions regarding the actual decision-making, this model is still commonly used in practice because it enables a high level of interpretability. Interpretability is critical for researchers and practitioners to get insights into the complex human decision-making process. For instance, linear specifications allow for straightforward derivation of the value-of-time (VOT), i.e., the marginal rate of substitution between time and cost, that constitutes a highly relevant measure in a wide range of public transport policy.