Smart Traffic Light Management System: A Novel Approach Using IoT and AI for Emergency Handling and Congestion Reduction

Abstract

City traffic congestion, together with ineffective traffic signals, continues to pose significant problems in metropolitan areas, which results in longer travel times and worsened pollution while also delaying emergency responses. This paper proposed a smart traffic light control system that implemented IoT alongside RFID and real-time camera detection of objects for enhancing emergency vehicle prioritization and managing traffic flow. The system continuously adapts traffic light intervals according to existing traffic amounts, which results in fewer jams and decreased waiting periods for all vehicles. Emergency vehicle detection through RFID technology directs ambulance and fire truck vehicles to safely access intersections without delays. The system utilized Arduino microcontrollers alongside ESP32-CAM modules and Python-based object detection algorithms for implementing the system that proved its success in handling complex traffic situations. The implemented system generated important results, which improved traffic efficiency while lowering vehicle emissions and accelerating emergency response service. AI, alongside IoT technologies, enables the system to bring transformative changes to how cities manage their urban traffic flow by delivering both expandable and eco-friendly solutions for smart cities. Intelligent traffic systems demonstrate essential value for tackling modern urbanization-related challenges as well as pollution problems and public safety issues.