Abstract In recent years, Intelligent Transportation Systems (ITS) have seen efficient and
faster development by implementing deep learning techniques in problem domains which …

Monitoring-enhanced resilience in transport management is emerging together with the new
technologies and digital data, however have not been fully explored yet. Digital technologies …

The new era of sharing information and “big data” has raised our expectations to make
mobility more predictable and controllable through a better utilization of data and existing …

High-granularity vehicle trajectory data can help researchers develop traffic simulation
models, understand traffic flow characteristics, and thus propose insightful strategies for road …

Unmanned Aerial Vehicles (UAVs) have immense potential to enhance Intelligent Transport
Systems (ITS) by aiding in real-time traffic monitoring, emergency response, and …

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

In the within-intersection area, vehicles from different approaches make turning movements
resulting in many conflict points. Hence, drivers are more prone to make mistakes in that …

To facilitate the monitoring and management of modern transportation systems, monocular
visual traffic surveillance systems have been widely adopted for speed measurement …

Vehicle trajectory data have become essential for many research fields, such as traffic flow,
traffic safety, and automated driving. To make trajectory data useable for researchers, an …

Traffic congestion occurs when traffic demand is greater than the available network capacity.
It is characterized by lower vehicle speeds, increased travel times, arrival unreliability, and …