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<p>Traffic signal control plays a crucial role in managing traffic flow and alleviating congestion on urban roads. This study proposes a deep reinforcement learning (DRL) approach to optimize traffic signal control and reduce traffic congestion in urban environments. Leveraging Perceiver transformers and a deep neural network, the approach uses traffic flow data — including speed, vehicle arrivals, and other relevant metrics — to enhance signal regulation. The DRL framework is based on the Q-learning algorithm and operates without relying on specific traffic models or rules. To evaluate different traffic control strategies, we introduce a benchmark map of Žilina city as a testing ground. Our method establishes a robust framework for optimising urban traffic, achieving significant improvements in traffic flow efficiency and congestion reduction, as demonstrated through simulations on this map.</p>

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Exploring Urban Traffic Dynamics: Introducing a Benchmark Map for Comprehensive Testing and Evaluation

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年6月13日

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  • Michal Skuba,
  • Aleš Janota,
  • Pavol Kuchár,
  • Júlia Kafková,
  • Marián Hruboš,
  • Mário Michálik,
  • Longrui Han,
  • Patrik Kovačovič

摘要

Traffic signal control plays a crucial role in managing traffic flow and alleviating congestion on urban roads. This study proposes a deep reinforcement learning (DRL) approach to optimize traffic signal control and reduce traffic congestion in urban environments. Leveraging Perceiver transformers and a deep neural network, the approach uses traffic flow data — including speed, vehicle arrivals, and other relevant metrics — to enhance signal regulation. The DRL framework is based on the Q-learning algorithm and operates without relying on specific traffic models or rules. To evaluate different traffic control strategies, we introduce a benchmark map of Žilina city as a testing ground. Our method establishes a robust framework for optimising urban traffic, achieving significant improvements in traffic flow efficiency and congestion reduction, as demonstrated through simulations on this map.