Traffic signal control is an important and challenging real-world problem that has recently
received a large amount of interest from both transportation and computer science …

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

While deep reinforcement learning (RL) has fueled multiple high-profile successes in
machine learning, it is held back from more widespread adoption by its often poor data …

Traffic signal control is an important and challenging real-world problem, which aims to
minimize the travel time of vehicles by coordinating their movements at the road …

Graph neural networks (GNNs) have been extensively used in a wide variety of domains in
recent years. Owing to their power in analyzing graph-structured data, they have become …

Multi-agent deep reinforcement learning (MDRL) has been widely applied in multi-
intersection traffic signal control. The MDRL algorithms produce the decentralized …

Deep reinforcement learning (RL) has been applied to traffic signal control recently and
demonstrated superior performance to conventional control methods. However, there are …

Traffic signal control is of great importance to the urban transportation systems and public
travel, yet it becomes challenging because of two essential factors. First, spatial–temporal …

Multi-agent reinforcement learning (MARL) is a promising algorithm for traffic signal control
(TSC), and graph neural networks make a further improvement on its learning capacity …

Numerous methods are proposed for the Traffic Signal Control (TSC) tasks aiming to provide
efficient transportation and mitigate congestion waste. In recent, promising results have …