Recently, development in intelligent transportation systems (ITS) requires the input of
various kinds of data in real-time and from multiple sources, which imposes additional …

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

Network traffic flow prediction on a fine-grained spatio-temporal scale is essential for
intelligent transportation systems, and extensive studies have been carried out in this area …

An alternative approach for real-time network-wide traffic control in cities that has recently
gained attention is perimeter flow control. Many studies have shown that this method is more …

Aggregated traffic flow models based on the Macroscopic Fundamental Diagram (MFD),
also known as multi-reservoir or multi-region MFD models, have been developed for more …

Controlling traffic in large-scale urban road networks is a challenging problem. Aggregated
dynamical models, based on the macroscopic fundamental diagram (MFD) of urban traffic …

Advancements in sensor technologies, vehicle automation, communication, and intelligent
transportation systems create unforeseen possibilities for the development of novel traffic …

Traffic management by applying congestion pricing is a measure for mitigating congestion in
protected city corridors. As a promising tool, pricing improves the level of service in a …

State observability and demand estimation are two main issues in large-scale traffic
networks which hinder real-world application of real-time control strategies. This study …

Recent studies on macroscopic fundamental diagram (MFD) have shifted towards
enhancing MFD dynamics in terms of efficiency and realism. In this paper, a multi-reservoir …