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Momentum Based Reward Design for Low Emission Traffic Signal Control

arXiv 2605.29693 ↗First posted 28 May 2026cs.LG cs.ROcited by 0

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Abstract (arXiv)

Urban traffic congestion is a growing global issue contributing significantly to long commute times and environmental pollution. Traditional traffic signal control systems often fail to adapt to dynamic traffic conditions. Adaptive traffic signal control can improve urban traffic without changing road infrastructure. Deep Reinforcement Learning (DRL) has shown strong performance for this task, but existing delay and queue-based rewards often produce short-sighted or unstable policies. This paper proposes a Momentum-Based Reward Function (MBRF) that encourages vehicles to keep moving rather than penalizing congestion alone. The method is evaluated in SUMO (Simulation of Urban MObility) using standard traffic metrics such as waiting time, queue length, throughput, and CO2 emissions. Results show that the proposed reward produces better throughput-emission trade-offs and more stable learning behavior than delay or queue-based rewards, as well as classical controllers such as Max Pressure and LQF.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 7 of its 23 references that are in the map. First posted on arXiv 28 May 2026.

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7 of 23 references.

References · 7

  1. 2019Multi-Agent Deep Reinforcement Learning for Large-scale Traffic Signal Controlcited by 164
  2. 2019A Survey on Traffic Signal Control Methodscited by 79
  3. 2018Deep Reinforcement Learning for Traffic Light Control in Vehicular Networkscited by 52
  4. 2017Traffic Light Control Using Deep Policy-Gradient and Value-Function Based Reinforcement Learningcited by 33
  5. 2019Learning an Interpretable Traffic Signal Control Policycited by 20
  6. 2023Deep Reinforcement Learning-based Intelligent Traffic Signal Controls with Optimized CO2 emissionscited by 2
  7. 2023Integrated Freeway Traffic Control Using Q-Learning with Adjacent Arterial Traffic Considerationscited by 1

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  1. 2024Reinforcement Learning for Adaptive Traffic Signal Control: Turn-Based and Time-Based Approaches to Reduce Congestion
  2. 2025Reinforcement Learning Based Traffic Signal Design to Minimize Queue Lengths
  3. 2026HiLLTS: Zero-Shot Hierarchical LLM-Guided Traffic Signal Control for Sustainable Transportation
  4. 2026Reinforcement Learning-Based Traffic Signal Control for IoT-Enabled Intersections
  5. 2022Learning Eco-Driving Strategies at Signalized Intersections
  6. 2023A Fully Data-Driven Approach for Realistic Traffic Signal Control Using Offline Reinforcement Learning
  7. 2025Joint Pedestrian and Vehicle Traffic Optimization in Urban Environments using Reinforcement Learning
  8. 2025Optimizing Efficiency of Mixed Traffic through Reinforcement Learning: A Topology-Independent Approach and Benchmark