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

Ta‐Yin Hu, Zhuo‐Yu Li

IET Intelligent Transport Systems 2024 article cited by 16

Source Open-access PDF More in Engineering & Materials

importance 6/10 ai assisted Traffic control and management traffic management stage: research

Problem

Coordinating traffic signals across multiple intersections to reduce congestion and waiting times.

Why it matters

Poor signal coordination wastes commuters' time and fuel and worsens urban congestion.

What the AI did

Deep reinforcement learning (Double Deep Q-Network) trained local agents per intersection, then a global agent unified their policies for signal coordination, tested in SUMO simulation.

Results

Claims improved intersection efficiency and minimized average waiting time; no numbers in abstract

Impact

Validated only in simulation; no real-world deployment stated.

Record

Venue	IET Intelligent Transport Systems
Published	2024
Work type	article
Authors	Ta‐Yin Hu, Zhuo‐Yu Li
Citations	16 (OpenAlex, at import time)
References	22
Model or method	Double Deep Q-Network (multi-agent DRL)
Organization	not stated in source
Primary source	https://doi.org/10.1049/itr2.12521
Open access	gold
Keywords	traffic signals, deep reinforcement learning, multi-agent, Double DQN, simulation

Importance rating

6 / 10

significant — thousands to millions affected

Signal control allocates time for competing traffic flows; poor coordination wastes commuter time and worsens congestion, but the abstract gives no scale or impact figures, and benefits are shown only in SUMO simulation.

Basis: inferred. Judged from the source text, which gives no explicit scale. Stated scale: not stated in source. Method and limits.

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