Traffic Light Control with Reinforcement Learning
Not on the map. Why?
Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 11 of its 47 references that are in the map. First posted on arXiv 28 Aug 2023.
Builds on
11 of 47 references.
References · 11
- 2017A Brief Survey of Deep Reinforcement Learningcited by 483
- 2019CoLight: Learning Network-level Cooperation for Traffic Signal Controlcited by 138
- 2021Recent Advances in Reinforcement Learning in Financecited by 110
- 2020Deep Reinforcement Learning for Intelligent Transportation Systems: A Surveycited by 98
- 2019Learning Phase Competition for Traffic Signal Controlcited by 87
- 2019A Survey on Traffic Signal Control Methodscited by 79
- 2016Using a Deep Reinforcement Learning Agent for Traffic Signal Controlcited by 26
- 2017Adaptive Traffic Signal Control: Deep Reinforcement Learning Algorithm with Experience Replay and Target Networkcited by 16
- 2020Calibrating Path Choices and Train Capacities for Urban Rail Transit Simulation Models Using Smart Card and Train Movement Datacited by 3
- 2023Individual Path Recommendation Under Public Transit Service Disruptions Considering Behavior Uncertaintycited by 2
- 2022Robust Path Recommendations During Public Transit Disruptions Under Demand Uncertaintycited by 1
Built on by
Citing papers per year: 2025: 1.
1 paper.