Development of new approaches to adaptive traffic signal control has received significant
attention; an example is the reinforcement learning (RL), where training and implementation …

Controlling traffic signals to alleviate increasing traffic pressure is a concept that has
received public attention for a long time. However, existing systems and methodologies for …

Adaptive traffic signal control systems are deployed to accommodate real-time traffic
conditions. Yet travel demand and behavior of the individual vehicles might be overseen by …

Autonomous intersection management has the ability to reduce congestion at intersections
significantly, compared to classical traffic signal control in the era of connected autonomous …

Connected Autonomous Vehicles will make autonomous intersection management a reality
replacing traditional traffic signal control. Autonomous intersection management requires …

In the future, mixed traffic flow will include two types of vehicles: connected autonomous
vehicles (CAVs) and human-driven vehicles (HDVs). CAVs emerge as new solutions to …

Adaptive traffic signals equipped with sensors are becoming increasingly important in
enhancing the efficiency of existing transportation networks. Machine learning techniques …

Due to its capability in handling complex urban intersection environments, deep
reinforcement learning (DRL) has been widely applied in Adaptive Traffic Signal Control …

Reinforcement learning (RL) approaches have recently been spotlighted for use in adaptive
traffic-signal control on an area-wide level. Most researchers have employed multi-agent …

Adaptive traffic signal control techniques have been developed in numerous studies to
increase traffic flow efficiency. Using traffic signals to design an adaptive traffic management …