IoT-Based Smart Traffic Signal System for Ambulance Prioritization

Abstract

One Ambulance delay at traffic intersections is one of the major challenges affecting emergency response time in metropolitan cities. This paper presents an IoT-based Smart Traffic Signal System for Ambulance Prioritization that integrates artificial intelligence (AI) with IoT control using an ESP32 microcontroller to automatically provide green signal priority for ambulances. A camera-based AI module continuously analyzes live video using OpenCV and a deep-learning model (YOLOv8) to detect ambulances based on vehicle patterns, color, and emergency markings. Once an ambulance is identified, the AI server sends a Wi-Fi command to the ESP32 controller, which operates the LEDs representing traffic lights. The corresponding green signal is activated to clear the path for the ambulance while other directions remain red. For reliability, an LM393 sound sensor is installed near the junction to detect ambulance sirens. If the network or AI module fails, the sound sensor automatically triggers the green signal through the ESP32, ensuring a fail-safe operation. This dual-detection approach (vision + sound) enhances system accuracy and reliability. The proposed model provides a cost-effective, automated, and intelligent solution that improves ambulance response time and minimizes manual intervention. It is easy to deploy, scalable across multiple junctions, and contributes to the development of smart city infrastructure for real-time emergency management.