Distributed Signal Control for Multi-Modal Traffic Network: A Deep Reinforcement Learning Approach

Yu, Jiajie; Laharotte, Pierre-Antoine; Leclercq, Ludovic

doi:10.2139/ssrn.4291480

Cited by 1 publication

(2 citation statements)

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Section: Literature Reviewmentioning

confidence: 99%

“…Yu et al [26] described that inefficient traffic signals and bus headway control strategies can lead to significant problems, including traffic congestion, bus passenger delays, and bus crowding. Therefore, Yu et al [26] aimed to manage traffic flow at intersections within large-scale networks, solving the challenge of integrating various objectives. At each decision step, the agents strive to achieve two objectives simultaneously: reducing the total stopping time for vehicles and equalizing the space headways ahead of and behind buses when they approach intersections.…”

Section: Literature Reviewmentioning

confidence: 99%

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A multi‐agent deep reinforcement learning approach for traffic signal coordination

Hu,

2024

IET Intelligent Trans Sys

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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.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%