Before revision:  DNN models such as the Multi-layer Perceptron (MLP)  have shown remarkable success in modelling complex data accurately, but recent studies have consistently demonstrated that their black-box properties are incompatible with discrete choice analysis for the purpose of interpreting decision making behaviour.Our proposed machine learning choice model is a departure from the conventional feed-forward MLP framework by using a dynamic residual neural network learning based approach. Our proposed method can be formulated as a Generalized Extreme Value (GEV) random utility maximization model for greater flexibility in capturing unobserved heterogeneity.It can generate choice model structures where the covariance between random utilities is estimated and incorporated into the random error terms, allowing for a richer set of higher-order substitution patterns than a standard logit might be able to achieve .
After revision:  DNN models such as the Multi-layer Perceptron (MLP) have shown remarkable success in modelling complex data accurately, but recent studies have consistently demonstrated that their 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 .