An IoT-Enabled System for Prioritizing Emergency Vehicles and Detecting Accidents in Smart Traffic Management

Abstract—With the growth of urban areas and the rising number of vehicles, managing traffic congestion has become increasingly difficult, especially when it comes to ensuring prompt clearance for emergency vehicles like ambulances, fire trucks, and police cars. This paper introduces a Smart Traffic Management System powered by IoT, which leverages camera modules and machine learning algorithms to identify and prioritize emergency vehicles. Ambulances receive the highest priority, followed by fire trucks and police vehicles. The system adapts traffic signals dynamically by detecting emergency vehicles in real-time, and also enhances traffic flow using IR sensors to monitor vehicle density. Additionally, the system includes an Accident Detection Module that leverages intelligent accelerometer sensors to detect road accidents, using data on speed, pressure, and distance. This holistic approach ensures that emergency lanes are cleared efficiently and improves traffic mobility in urban environments. The proposed system significantly enhances the efficiency of traffic management compared to conventional methods, offering a comprehensive solution for reducing congestion, improving emergency response times, and minimizing accident fatalities. Index Terms—IoT, smart traffic management, machine learning, emergency vehicle detection, traffic signal optimization, IR sensors, accident detection, accelerometer sensors, real-time data processing, urban mobility, SCATS (Sydney Coordinated Adaptive Traffic System), IoT-Enabled Smart Traffic Management System (ISTMS).