Title: A Multi-agent Traffic Signal Control System Using Reinforcement Learning
Abstract: This paper presents a control method based on multi-agent for traffic signals. A reinforcement learning algorithm is used to optimize traffic flow in the intersection. The genetic algorithm intends to introduce a global optimization criterion to each of the local learning processes that optimize the cycle of traffic signals and green-ratio. Area-wide coordination is achieved by game theory. We combine local optimization with global optimization to optimize traffic signal in multi-intersection. Simulation results indicate that our presented method is superior than traditional control one.
Publication Year: 2009
Publication Date: 2009-01-01
Language: en
Type: article
Indexed In: ['crossref']
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Cited By Count: 3
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