ASTC: Adaptive Signal Traffic Control Using IoT Sensors and OCR-Based Emergency Detection

Existing urban traffic control continues to experience considerable difficulty from extremely dynamic traffic, recurring bottlenecks, and an acute need to clear emergency vehicles within a short time span. Pre-programmed fixed-time traffic signals do not provide flexibility to manage effectively all these issues, leading to wasted time, fuel, and delayed emergency responses. This paper presents an IoT-Based Smart Traffic Control System that integrates ultrasonic and infrared (IR) sensors with an ESP32-CAM module in an adaptive control framework. The system measures real-time traffic density using a composite metric from queue length and vehicle flow rate, enabling dynamic adjustment of green signal durations. An on-device Optical Character Recognition (OCR) mechanism detects emergency service markings (e.g., ambulances, fire trucks, police vehicles) and overrides standard signal cycles to grant immediate right-of-way. MQTT-based communications coordinate multiple intersections to prevent spillback congestion and grant smooth emergency passage. Laboratory testing within an urban simulation showed 25 % mean vehicle waiting time reduction and more than 97 % emergency clearance time improvement compared to fixed-time systems. The resolution is reasonable, energy-savvy as well as mountable, and boosts municipal security.