In recent years, the advancement of artificial intelligence techniques has led to significant
interest in reinforcement learning (RL) within the traffic and transportation community …

Advancements in digital automation for smart grids have led to the installation of
measurement devices like phasor measurement units (PMUs), micro-PMUs (-PMUs), and …

Despite significant progress in autonomous vehicles (AVs), the development of driving
policies that ensure both the safety of AVs and traffic flow efficiency has not yet been fully …

Abstract Model-based reinforcement learning (RL) is anticipated to exhibit higher sample
efficiency than model-free RL by utilizing a virtual environment model. However, obtaining …

Conventional reinforcement learning (RL) models of variable speed limit (VSL) control
systems (and traffic control systems in general) cannot be trained in real traffic process …

Bottleneck areas are prone to severe traffic congestion due to the sudden drop in capacity.
To improve traffic efficiency in the bottleneck area, this paper proposes a multi-agent deep …

To improve the traffic safety and efficiency of freeway tunnels, this study proposes a novel
variable speed limit (VSL) control strategy based on the model-based reinforcement …

The emergence of automated vehicles may have significant impacts on traffic flow. While
many studies suggest that fully automated vehicles can improve traffic flow by changing their …

Abstract System optimal traffic routing can mitigate congestion by assigning routes for a
portion of vehicles so that the total travel time of all vehicles in the transportation system can …

Variable speed limit (VSL) control plays a vital role in the emerging connected automated
vehicle highway (CAVH) system, which can alleviate recurrent traffic congestion caused by …