Accounting for some types of heterogeneity has been an important pathway to improving our
models in the transportation domain, specifically in travel behavior research. This study …

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

Abstract We present a Gaussian Process–Latent Class Choice Model (GP-LCCM) to
integrate a non-parametric class of probabilistic machine learning within discrete choice …

This study investigates the process of stage transition in traveler's adoption of carsharing.
The carsharing adoption behaviors are classified into five stages using the transtheoretical …

Abstract Latent Class Choice Models (LCCM) are extensions of discrete choice models
(DCMs) that capture unobserved heterogeneity in the choice process by segmenting the …

Today, cities seek to transition to more sustainable transportation modes. Cycling is critical
in this shift, promoting a more beneficial lifestyle for most. However, cyclists are exposed to …

Artificial neural networks are often criticized for their black box nature in travel behavior
literature. The lack of understanding of variable influence induces little confidence in model …

This article investigates the usage of a general model-based recursive partitioning algorithm
to model preference heterogeneity. We use the algorithm to grow a decision tree based on …

The potential of Machine Learning to complement, rethink, replace or improve choice
modeling has been the subject of many works throughout the last few years (Hagenauer and …

Economic policies aimed at achieving a sustainable society have been formulated in several
countries. To choose effective policies, individuals' behavior must be guided in line with …