  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.
