Before revision: We describe the process of our model estimation and examine the relative empirical performance and econometric implications on two mode choice experiments. We analyzed the behavioural and theoretical properties of our methodology.We showed how model interpretability is possible, while also capturing the underlying complex and unobserved behavioural heterogeneity effects in the residual covariance matrices .
After revision: We describe the process of our model estimation and examine the relative empirical 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 .