Traffic Light Control with Reinforcement Learning

• URL: http://arxiv.org/abs/2308.14295v1
• Date: Mon, 28 Aug 2023 04:29:49 GMT
• Title: Traffic Light Control with Reinforcement Learning
• Abstract summary: This paper proposes a real-time traffic light control method using deep Q learning. It incorporates a reward function considering queue lengths, delays, travel time, and throughput. The model dynamically decides phase changes based on current traffic conditions.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Traffic light control is important for reducing congestion in urban mobility systems. This paper proposes a real-time traffic light control method using deep Q learning. Our approach incorporates a reward function considering queue lengths, delays, travel time, and throughput. The model dynamically decides phase changes based on current traffic conditions. The training of the deep Q network involves an offline stage from pre-generated data with fixed schedules and an online stage using real-time traffic data. A deep Q network structure with a "phase gate" component is used to simplify the model's learning task under different phases. A "memory palace" mechanism is used to address sample imbalance during the training process. We validate our approach using both synthetic and real-world traffic flow data on a road intersecting in Hangzhou, China. Results demonstrate significant performance improvements of the proposed method in reducing vehicle waiting time (57.1% to 100%), queue lengths (40.9% to 100%), and total travel time (16.8% to 68.0%) compared to traditional fixed signal plans.

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