Abstract Machine Learning (ML) approaches are increasingly being investigated as an
alternative to Random Utility Models (RUMs) for modelling passenger mode choice. These …

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

While deep neural networks (DNNs) have been increasingly applied to choice analysis
showing high predictive power, it is unclear to what extent researchers can interpret …

Researchers have compared machine learning (ML) classifiers and discrete choice models
(DCMs) in predicting travel behavior, but the generalizability of the findings is limited by the …

In recent years, planners have started considering Machine Learning (ML) techniques as an
alternative to discrete choice models (CM). ML techniques are primarily data-driven and …

Abstract Artificial Neural Networks (ANNs) are rapidly gaining popularity in transportation
research in general and travel demand analysis in particular. While ANNs typically …

Since its inception, the choice modelling field has been dominated by theory-driven models.
The recent emergence and growing popularity of machine learning models offer an …

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 prediction of travel mode preference, like many other choice prediction problems, may
depend on categorical features of the choice options or the choice makers. Such categorical …

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 …