Smart Traffic Management: Enhancing Emergency Response with AI And IoT

Abstract

Traffic congestion and inefficient emergency vehicle management are significant challenges in urban transportation systems. Traditional traffic signals operate on fixed timing cycles, failing to adapt to real-time conditions, leading to delays for emergency vehicles and increased congestion. This paper proposes an AI-based intelligent traffic management system integrating RFID, IoT, motion sensors, and machine learning to dynamically control traffic signals and prioritize emergency vehicles. RFID technology detects ambulances, fire trucks, and VIP vehicles, triggering an automated green signal while opposing signals turn red. Ultrasonic sensors continuously monitor traffic density, ensuring adaptive signal control based on real-time congestion. The system also leverages AI-driven predictive models to forecast peak traffic hours and optimize signal timings proactively. Additionally, IoT-enabled cloud connectivity provides real-time monitoring and data analytics for traffic authorities, improving decision-making and urban mobility. Experimental results demonstrate significant improvements in emergency response times, congestion reduction, and adaptive traffic signal efficiency. The proposed system offers a scalable, intelligent, and sustainable traffic management solution, contributing to the development of smart cities and efficient urban transportation.