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

The research question this review focused on is “What are the factors that influence
commuters' willingness to ride an integrated public transport (PT) system?”. Transfers are a …

In the field of transportation, data analysis is probably the most important and widely used
research tool available. In the data analysis universe, there are two 'schools of thought'; the …

Sustainability policies to mitigate transportation energy impacts on the urban environment
are urgently needed. Energy prediction models provide critical information to decision …

The field of travel demand analysis has traditionally been dominated by statistical models.
Conversely, Machine Learning (ML) techniques have been rapidly emerging in the past few …

In recent decades, transportation planning researchers have used diverse types of machine
learning (ML) algorithms to research a wide range of topics. This review paper starts with a …

The research aims to choose a strategy for transport companies' behavior in the context of
the development of Industry 4.0. It is proposed to use a mini-maximum model as an …

Out-of-vehicle times were shown to be perceived as being more onerous than in-vehicle
time by transit users when making transfers. The present study has two main objectives. The …

The discrete choice modeling and decisions-tree technique are used to understand the
travel behavior of people in Budapest. The discrete choice modeling is applied to develop …

Travel mode choice (TMC) prediction is crucial for transportation planning. Most previous
studies have focused on TMC in adults, whereas predicting TMC in children has received …