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

Abstract Mobility-as-a-Service (MaaS) system is regarded as one of the emerging solutions
to offer integrated, seamless, and flexible multi-modal mobility services as an alternative to …

Tourist travel contributes greatly to transport problems in highly attractive tourist areas.
Despite that, local travel behavior of visitors at the destination has hardly been investigated …

To deal with increasing amounts of data, decision and policymakers frequently turn to
advances in machine learning and artificial intelligence to capitalise on the potential reward …

Activity-based travel demand models (ABM) have gained popularity during the last three
decades and are currently in use in several urban areas. In ABM, special emphasis is given …

The growing popularity of Mobility as a Service (MaaS) has led researchers to realize that it
can be used for the optimal management of the transportation system of a city. This study is …

Background and aim The interest in investigating transportation issues at university
campuses has been growing worldwide. Given the university travelers' unique travel …

Freeway service areas (FSA) provide a variety of services for road users, but these services
in some cases do not guarantee the satisfaction of road users. Understanding the factors …

Individuals' participation in daily routine activities varies according to the situations they live
in. Gender roles and marital status drive them to take part in various economic, leisure and …

This paper presents an episode-level activity generation and destination choice model. The
model accommodates the occurrence of multiple episodes of an activity and captures …