Ensemble forecasting is a modeling approach that combines data sources, models of
different types, with alternative assumptions, using distinct pattern recognition methods. The …

Because two decades have almost passed since the introduction of activity-based models of
travel demand, this seems the right time to evaluate progress made in the development and …

Some recent studies have shown that machine learning can achieve higher predictive
accuracy than logit models. However, existing studies rarely examine behavioral outputs …

The analysis of travel mode choice is important in transportation planning and policy-making
in order to understand and forecast travel demands. Research in the field of machine …

The analysis of travel mode choice is an important task in transportation planning and policy
making in order to understand and predict travel demands. While advances in machine …

Very few studies have examined the impact of built environment on urban rail transit
ridership at the station-to-station (origin-destination) level. Moreover, most direct ridership …

Deep Learning methods have been proven to be flexible to model complex phenomena.
This has also been the case of Intelligent Transportation Systems, in which several areas …

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

Many global high-density cities have embraced transit-oriented development (TOD)
strategies around metro stations in a strong push toward promoting transit trips. However …

Understanding choice behavior regarding travel mode is essential in forecasting travel
demand. Machine learning (ML) approaches have been proposed to model mode choice …