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As urbanization accelerates, traffic congestion presents a significant challenge for smart cities, impacting mobility and air quality. The paper is all about traffic management solution using reinforcement learning (RL) for real-time traffic control. Our system is set to change the signal timings based on traffic conditions, pedestrian movements, and environmental factors learning it lively and accurately. Deploying a multi-layered architecture, where the medium manages their role unitedly to enhance traffic flow. Feedback mechanisms are there to process the model for effective intermediation. Simulations project that the approach is noticeable in reducing travel times and crowding. This research helps in the advancement of smarter, more flexible urban transportation systems, assisting ability to move and making the urban life easier.

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A Tactical Traffic Management Solution for Smart Cities Using Reinforcement Learning

  • 会议论文

  • 发表日期：

    2025年

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  • Arkaprabha Rakshit,
  • Pritam Karmakar,
  • Sushruta Mishra,
  • Tiansheng Yang,
  • Ruikai Sun,
  • Rajkumar Singh Rathore

摘要

As urbanization accelerates, traffic congestion presents a significant challenge for smart cities, impacting mobility and air quality. The paper is all about traffic management solution using reinforcement learning (RL) for real-time traffic control. Our system is set to change the signal timings based on traffic conditions, pedestrian movements, and environmental factors learning it lively and accurately. Deploying a multi-layered architecture, where the medium manages their role unitedly to enhance traffic flow. Feedback mechanisms are there to process the model for effective intermediation. Simulations project that the approach is noticeable in reducing travel times and crowding. This research helps in the advancement of smarter, more flexible urban transportation systems, assisting ability to move and making the urban life easier.