Many studies confirmed that a proper traffic state representation is more important than
complex algorithms for the classical traffic signal control (TSC) problem. In this paper, we (1) …

For the multi-agent traffic signal controls, the traffic signal at each intersection is controlled
by an independent agent. Since the control policy for each agent is dynamic, when the traffic …

Traffic signal control (TSC) is still one of the most significant and challenging research
problems in the transportation field. Reinforcement learning (RL) has achieved great …

Traffic Signal Control (TSC) aims to reduce the average travel time of vehicles in a road
network, which in turn enhances fuel utilization efficiency, air quality, and road safety …

Existing inflexible and ineffective traffic light control at a key intersection can often lead to
traffic congestion due to the complexity of traffic dynamics, how to find the optimal traffic light …

The control of traffic signals is crucial for improving transportation efficiency. Recently,
learning-based methods, especially Deep Reinforcement Learning (DRL), garnered …

The application of reinforcement learning in traffic signal control (TSC) has been extensively
researched and yielded notable achievements. However, most existing works for TSC …

Efficient traffic signal control is critical for reducing traffic congestion and improving overall
transportation efficiency. The dynamic nature of traffic flow has prompted researchers to …

The acceleration of urbanization has led to increasingly severe traffic congestion, creating
an urgent need for effective traffic signal control strategies to improve road efficiency. This …

With the development of artificial intelligence techniques, transportation system optimization
is evolving from traditional methods relying on expert experience to simulation and learning …