Recent advances in reinforcement learning for traffic signal control: A survey of models and evaluation
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 …
received a large amount of interest from both transportation and computer science …
[HTML][HTML] Reinforcement learning in urban network traffic signal control: A systematic literature review
Improvement of traffic signal control (TSC) efficiency has been found to lead to improved
urban transportation and enhanced quality of life. Recently, the use of reinforcement …
urban transportation and enhanced quality of life. Recently, the use of reinforcement …
Deep reinforcement learning for intelligent transportation systems: A survey
Latest technological improvements increased the quality of transportation. New data-driven
approaches bring out a new research direction for all control-based systems, eg, in …
approaches bring out a new research direction for all control-based systems, eg, in …
A survey on traffic signal control methods
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 …
minimize the travel time of vehicles by coordinating their movements at the road …
Learning phase competition for traffic signal control
Increasingly available city data and advanced learning techniques have empowered people
to improve the efficiency of our city functions. Among them, improving urban transportation …
to improve the efficiency of our city functions. Among them, improving urban transportation …
An experimental review of reinforcement learning algorithms for adaptive traffic signal control
Urban traffic congestion has become a serious issue, and improving the flow of traffic
through cities is critical for environmental, social and economic reasons. Improvements in …
through cities is critical for environmental, social and economic reasons. Improvements in …
Reinforcement learning benchmarks for traffic signal control
We propose a toolkit for developing and comparing reinforcement learning (RL)-based traffic
signal controllers. The toolkit includes implementation of state-of-the-art deep-RL algorithms …
signal controllers. The toolkit includes implementation of state-of-the-art deep-RL algorithms …
Network-wide traffic signal control based on the discovery of critical nodes and deep reinforcement learning
To improve the traffic efficiency of city-wide road networks, we propose a traffic signal control
framework that prioritizes the optimal control policies on critical nodes in road networks. In …
framework that prioritizes the optimal control policies on critical nodes in road networks. In …
Traffic signal optimization through discrete and continuous reinforcement learning with robustness analysis in downtown Tehran
Traffic signal control plays a pivotal role in reducing traffic congestion. Traffic signals cannot
be adequately controlled with conventional methods due to the high variations and …
be adequately controlled with conventional methods due to the high variations and …
Computing convex coverage sets for faster multi-objective coordination
In this article, we propose new algorithms for multi-objective coordination graphs (MO-
CoGs). Key to the efficiency of these algorithms is that they compute a convex coverage set …
CoGs). Key to the efficiency of these algorithms is that they compute a convex coverage set …