  This paper presents a novel deep learning-based travel behaviour choice
model.Our proposed Residual Logit (ResLogit) model formulation seamlessly
integrates a Deep Neural Network (DNN) architecture into a multinomial logit
model. Recently, DNN models such as the Multi-layer Perceptron (MLP) and the
Recurrent Neural Network (RNN) have shown remarkable success in modelling
complex and noisy behavioural data. However, econometric studies have argued
that machine learning techniques are a `black-box' and difficult to interpret
for use in the choice analysis.We develop a data-driven choice model that
extends the systematic utility function to incorporate non-linear cross-effects
using a series of residual layers and using skipped connections to handle model
identifiability in estimating a large number of parameters.The model structure
accounts for cross-effects and choice heterogeneity arising from substitution,
interactions with non-chosen alternatives and other effects in a non-linear
manner.We describe the formulation, model estimation, interpretability and
examine the relative performance and econometric implications of our proposed
model.We present an illustrative example of the model on a classic red/blue bus
choice scenario example. For a real-world application, we use a travel mode
choice dataset to analyze the model characteristics compared to traditional
neural networks and Logit formulations.Our findings show that our ResLogit
approach significantly outperforms MLP models while providing similar
interpretability as a Multinomial Logit model.
