Intelligent Traffic Signal Control Using Reinforcement Learning for Adaptive Urban Traffic Management

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@article{1717872,
      author = {Dixit Kachhadiya, Dr. M. N. Nachappa},
      title = {Intelligent Traffic Signal Control Using Reinforcement Learning for Adaptive Urban Traffic Management},
      journal = {Iconic Research And Engineering Journals},
      year = {2026},
      volume = {9},
      number = {11},
      pages = {2370-2377},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1717872.pdf},
      abstract = {Traffic congestion has become one of the most important problems in modern cities due to a rise in population, the number of vehicles owned, and the ineffective traffic management systems. Conventional traffic signal control mechanisms generally function under fixed time intervals, which do not take real time traffic conditions into account. As a result, there is often unnecessary waiting time for the vehicles, and traffic is severely congested, and the fuel consumption is high and the environmental pollution is intense. In order to overcome these challenges, intelligent traffic management solutions based on Artificial Intelligence (AI) and Machine Learning (ML) have emerged as promising solutions to improve urban transportation systems. This research presents an intelligent traffic signal control research proposal that uses Reinforcement Learning (RL) algorithm to dynamically adjust the traffic signal timing based on the real-time traffic condition. Reinforcement Learning lets the system learn the best strategy for controlling the traffic through its continuous interaction with the traffic environment. The proposed model analyses the important traffic factors such as the queue length of vehicles, the density of the traffic, and the waiting time of vehicles to make experienced decisions for the adaptive traffic signals. The effectiveness of the suggested reinforcement learning based traffic signal system control is tested by comparing the system performance with the traditional traffic signal control system based on the fixed time. Performance indicators such as average waiting time for vehicles, long queues are used to measure the system's efficiency and traffic throughput. The expected results on the reinforcement learning approach are that it would be able to greatly improve the efficiency of traffic flow, eliminate congestion, and overall improvement of urban traffic flow management. This study supports the promotion of intelligent transportation systems and also helps in the progress of smarter and more sustainable traffic management solutions to cities.},
      month = {May},
      doi = {https://doi.org/10.64388/IREV9I11-1717872}
  }