Travel mode choice modelling plays a critical role in predicting passengers’ travel demand and planning local transportation systems. Researchers commonly adopt classical Random Utility Models to analyse individual decision-making based on the utility theory. Recently, with an increasing interest in applying Machine Learning techniques, a number of studies have used these methods for modelling travel mode preferences for their excellent predictive power. However, none of these studies proposes machine learning models that investigate the regional heterogeneity of travel behaviours. To address this gap, this study develops a Random Effect-Bayesian Neural Network (RE-BNN) framework to predict and explain travel mode choice across multiple regions by combining the Random Effect (RE) model and the Bayesian Neural Networks (BNN). The results show that this model outperforms the plain Deep Neural Network (DNN) regarding prediction accuracy and is more robust across different datasets. In addition, in terms of interpretation, the capability of RE-BNN to learn the travel behaviours across regions has been demonstrated by offset utilities, choice probability functions and local travel mode shares
