AI Driven Traffic Management System

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@article{1707604,
      author = {Aditya Takawale, Shreyas Wakhare, Soham Walimbe, Madhuri Thorat},
      title = {AI Driven Traffic Management System},
      journal = {Iconic Research And Engineering Journals},
      year = {2025},
      volume = {8},
      number = {9},
      pages = {1650-1655},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1707604.pdf},
      abstract = {In this paper, an enhanced AI-driven system to ease urban traffic jams is discussed with the help of real- time object detection and tracking features. Utilizing latest algorithms like YOLOv8 and DeepSORT, the system aptly observes traffic flow and grants higher priority for emergency vehicle passage. The model adjusts traffic light timing dynamically in a suggested framework, immensely enhancing traffic efficiency, lessening delay, fuel consumption, and emissions. Elaborate tests prove the capability of improve urban mobility and enable sustainable development.},
      keywords = {Artificial Intelligence, Traffic Optimization, YOLOv8, Deep Learning, Emergency Response, Sustainable Transportation},
      month = {March},
  }