Detecting, predicting, and alleviating traffic congestion are targeted at improving the level of
service of the transportation network. With increasing access to larger datasets of higher …

Network macroscopic fundamental diagrams (NMFD) and related network-level traffic
dynamics models have received both theoretical support and empirical validation with the …

The spatio-temporal imbalance of parking demand and supply results in unwanted on-street
cruising-for-parking traffic of conventional vehicles. Autonomous vehicles (AVs) can self …

As an active performance evaluation method, the fluid-based queueing model plays an
important role in traffic flow modeling and traffic state estimation problems. A critical …

Most feedback perimeter control approaches that are based on the Macroscopic
Fundamental Diagram (MFD) and are tested in detailed network structures restrict inflow …

Traffic control for large-scale urban road networks remains a challenging problem.
Aggregated dynamical models of city-scale traffic, based on the macroscopic fundamental …

Local traffic control schemes fall short of achieving coordination with other parts of the urban
road network, whereas a centralized controller based on the detailed traffic models would …

Aggregated network level modeling and control of traffic in urban networks have recently
gained a lot of interest due to unpredictability of travel behaviors and high complexity of …

The problem of clustering in urban traffic networks has been mainly studied in static
framework by considering traffic conditions at a given time. Nevertheless, it is important to …

Existing data-driven and feedback traffic control strategies do not consider the heterogeneity
of real-time data measurements. Besides, traditional reinforcement learning (RL) methods …