Abstract
The insatiable demand for mobility has recently created more complex challenges in urban areas, which can only be tackled by utilizing Intelligent Transportation Systems. These systems are also essential for reducing emissions, which has become a significant concern thanks to the continuously increasing number of vehicles on the roads. This paper proposes a Multi-Agent Reinforcement Learning based solution for solving the Traffic Signal Control problem in the case of six interconnected intersections. The Traffic Signal Control problem is chosen since signalized intersections are the bottlenecks of urban areas from the aspect of congestion. This paper aims to control the network more efficiently, considering sustainability and classic measures. The results show that the proposed approach can decrease, for example, fuel consumption by 2.2% and average travel time by 5.2%.
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