Since its inception, the choice modelling field has been dominated by theory-driven
modelling approaches. Machine learning offers an alternative data-driven approach for …

Mixed Logit is an advanced and flexible tool for the study of discrete choice problems.
However, this flexibility involves computationally intensive calculations, as the estimation of …

Abstract Background Discrete choice models (DCMs) for moral choice analysis will likely
lead to erroneous model outcomes and misguided policy recommendations, as only some …

This paper focuses on the discrete choice estimation problem, which involves multiple
objectives and testing a broad range of hypotheses that can affect both interpretability and …

Estimation of discrete outcome specifications involves significant hypothesis testing,
including multiple modelling decisions which could affect results and interpretation. Model …

The poor predictability and the misspecification arising from hand-crafted utility functions are
common issues in theory-driven discrete choice models (DCMs). Data-driven DCMs improve …

This study explored the impact of various label information (extrinsic attributes) and
sociodemographic and attitudinal factors (intrinsic attributes) on Brazilian consumer choices …

Individual-level modeling is an essential requirement for effective deployment of smart urban
mobility applications. Mode choice behavior is also a core feature in transportation planning …

Understanding mobility dynamics and their influence on epidemic spread is crucial for
effective management strategies, a concept that, despite its importance, has received limited …

Several non-linear functions and machine learning methods have been developed for
flexible specification of the systematic utility in discrete choice models. However, they lack …