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

From the perspective of historical review, the methodology of economics develops from
qualitative to quantitative, from a small sampling of data to a vast amount of data. Because of …

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

Origin-Destination (OD) travel demand prediction is a fundamental challenge in
transportation. Recently, spatial-temporal deep learning models demonstrate the …

In the past decade, transportation network companies (TNCs) such as Uber, Lyft, and Via
have established themselves as a viable transportation alternative to other modes. However …

Whereas deep neural network (DNN) is increasingly applied to choice analysis, it is
challenging to reconcile domain-specific behavioral knowledge with generic-purpose DNN …

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 …

Route choice modeling is a fundamental task in transportation planning and demand
forecasting. Classical methods generally adopt the discrete choice model (DCM) framework …

Although researchers increasingly adopt machine learning to model travel behavior, they
predominantly focus on prediction accuracy, ignoring the ethical challenges embedded in …

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 …