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Improving traffic light systems using Deep Q-networks
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DOI：10.1016/j.eswa.2024.124178.png)
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Abstract
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As our cities become more complex and traffic demand grows, managing such traffic efficiently becomes challenging. Hence, solutions that allow building upon the current traffic light systems and that can be readily deployed are of global interest. In this work, we address the challenge of improving traffic light management at intersections. We propose an agent -based traffic light control system where an agent, one per intersection, dynamically regulates the light's phase cycle depending on the current traffic conditions. To this end, we will rely on Deep Networks to adequately train agents to make good decisions. Simulation results in a realistic scenario using SUMO show that our proposed approach can significantly reduce waiting times, improving transit times by 44% compared to the standard fixed -timing method. Additionally, to assess the effectiveness and reliability of our control algorithm, we introduce new performance metrics.
Keywords:
Deep-Q networks
Urban traffic optimization
Neural network
SUMO
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