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<p> Traffic flows and emergency situations become very difficult to handle by traditional systems as cities become more crowded. The study suggests using both IoT and AI in traffic control to boost the way cities move people. Integrating computer vision and embedded systems into the system allows drivers to be detected instantly and traffic signals to adjust accordingly. By applying YOLOv8 and ESP32, the proposed device uses vehicle counts to change its signals and expect when emergency response is required, so people and machines are safe—without any human supervision. The purpose is to reduce traffic for all drivers and first responders and build smart city infrastructure that can expand as needed. Experiments show that prototypes can handle more traffic, reduce the number of emissions and make cities easier to move through.</p>

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AI Powered Traffic Management System

  • 期刊论文

  • 发表日期：

    2026年7月21日

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  • Rajiv Kumar Nath,
  • Arshita Pal,
  • Garv Makhija,
  • Inderpreet Kaur

摘要

Traffic flows and emergency situations become very difficult to handle by traditional systems as cities become more crowded. The study suggests using both IoT and AI in traffic control to boost the way cities move people. Integrating computer vision and embedded systems into the system allows drivers to be detected instantly and traffic signals to adjust accordingly. By applying YOLOv8 and ESP32, the proposed device uses vehicle counts to change its signals and expect when emergency response is required, so people and machines are safe—without any human supervision. The purpose is to reduce traffic for all drivers and first responders and build smart city infrastructure that can expand as needed. Experiments show that prototypes can handle more traffic, reduce the number of emissions and make cities easier to move through.