The purpose of signal control is to allocate time for competing traffic flows to ensure safety. Artificial intelligence has made transportation researchers more interested in adaptive traffic signal control, and recent literature confirms that deep reinforcement learning (DRL) can be effectively applied to adaptive traffic signal control. Deep neural networks enhance the learning potential of reinforcement learning. This study applies the DRL method, Double Deep Q‐Network, to train local agents. Each local agent learns independently to accommodate the regional traffic flows and dynamics. After completing the learning, a global agent is created to integrate and unify the action policies selected by each local agent to achieve the purpose of traffic signal coordination. Traffic flow conditions are simulated through the simulation of urban mobility. The benefits of the proposed approach include improving the efficiency of intersections and minimizing the overall average waiting time of vehicles. The proposed multi‐agent reinforcement learning model significantly improves the average vehicle waiting time and queue length compared with the results from PASSER‐V and pre‐timed signal setting strategies.
A multi‐agent deep reinforcement learning approach for traffic signal coordination
IET Intelligent Transport Systems ; 18 , 8 ; 1428-1444
2024-08-01
17 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
A multi‐agent deep reinforcement learning approach for traffic signal coordination
DOAJ | 2024
|Coordination and communication in deep multi-agent reinforcement learning
BASE | 2022
|Multi-agent road traffic signal control method based on deep reinforcement learning algorithm
Europäisches Patentamt | 2023
|