The project "Econometric Machine Learning for Better Heterogeneity Representation (Econ-ML)" was motivated by the need for accurate, scalable, and interpretable behavioural models to understand and predict individual decision-making processes. This is particularly important in transportation and mobility, where understanding travellers’ behaviours is essential for designing sustainable and efficient systems. Traditional econometric approaches, such as Discrete Choice Models (DCMs), have long been used to model decision-making but face limitations when handling the complexity of large-scale data and capturing nuanced behavioural heterogeneity. Recent advances in machine learning (ML) offer promising solutions by enabling flexible, non-linear modelling and generative capabilities. However, these techniques often lack the interpretability and theoretical grounding that econometric models provide.
The project aimed to bridge this gap by combining the strengths of machine learning with the robust theoretical foundation of econometrics. Specifically, it sought to develop hybrid modelling frameworks that integrate ML techniques, such as Variational Autoencoders (VAEs), into econometric models like Latent Class Choice Models (LCCMs) and Mixed Logit Models. These hybrid approaches were designed to enhance traditional behavioural choice models by improving out-of-sample generalization, generating synthetic data, imputing missing data, providing a more accurate representation of heterogeneity, while maintaining interpretability consistent with economic theory.
By applying these advanced methodologies to real-world transport data, the project aimed to generate new insights into travellers’ behaviours and contribute to the broader field of transport modelling. The project’s goal was to improve the quality and scalability of decision-support tools for policymakers and transport planners. The developed models can be also applied beyond transportation, with potential applications in other fields such as marketing, finance, economics, healthcare, and environmental economics, where understanding and predicting human behaviour are equally critical.
