Determining appropriate utility specifications for discrete choice models is time-consuming
and prone to errors. With the availability of larger and larger datasets, as the number of …

The emergence of a variety of Machine Learning (ML) approaches for travel mode choice
prediction poses an interesting question to transport modellers: which models should be …

Although researchers increasingly use deep neural networks (DNN) to analyze individual
choices, overfitting and interpretability issues remain obstacles in theory and practice. This …

To address the problem of climate change emissions from the transport sector, many
countries are promoting electric vehicles (EVs). To support such efforts, it is essential to …

To better understand the choice behavior of train route schedulers and to predict their
choices for optimizing the annual construction schedule, prospective data for 2020 on train …

This study presents a Latent Class Choice Model (LCCM) with a flexible class membership
component. Specifically, it formulates the latent classes using Gaussian-Bernoulli mixture …

To promote electric vehicles, it is vital to know what impacts the preferences for electric
vehicles over conventional fuel-based cars. To address this, a discrete choice experiment is …

This paper proposes a new method to combine choice-and text data to infer moral
motivations from people's actions. To do this, we rely on moral rhetoric, in other words …

To address the adverse effects of urban freight, downshifting to smaller vehicles, such as
cargo bikes, has been indicated as a viable solution. Existing literature on freight mode …

Predicting and understanding travellers' mode choices is crucial to developing urban
transportation systems and formulating traffic demand management strategies. Machine …