Abstract Mobility as a Service (MaaS) has been gaining popularity as a result of massive
information and communication technology development in the transport sector. Studies …

The COVID-19 pandemic strongly affected mobility around the world. Public transport was
particularly hindered, since people may perceive it as unsafe and decide to avoid it …

The excessive traffic congestion in vehicles lowers the service quality of urban bus system,
reduces the social distance of bus passengers, and thus, increases the spread speed of …

Public transport networks are affected daily by disturbances with different entities. While big
service disturbances (or disruptions) are rare, delays and cancelled runs are more frequent …

Although urban rail transit systems play a crucial role in urban mobility, they frequently suffer
from unexpected disruptions due to power loss, severe weather, equipment failure, and …

Sensor and machine learning technologies have improved the perception of traffic systems
by providing detailed data about individual vehicle trajectories. Combining data from …

Predictions of transit delays are crucial to passengers and operators. Passengers utilize the
predictions to decide on departure time, route choice, and mode choice, whereas operators …

This paper uses unlabelled GPS tracking data collected by a smartphone application,
enriched by fusion with automatic vehicle location (AVL) data, to study commuting trips from …

Having access to realistic and empirically grounded passenger valuations of public transport
trip components facilitate the undertaking of necessary trade-offs during planning of …

Deep learning has been recently applied as an alternative method for several choice
problems, such as mode choice. Nevertheless, this method has not been particularly …