IOT-BASED INTELLIGENT TRAFFIC LIGHT DETECTION AND CONTROL SYSTEM USING ESP32, SENSORS, AND CLOUD MONITORING (BLYNK)

Abstract

Traffic congestion has become a critical challenge in modern urban environments, leading to increased travel time, fuel consumption, and environmental pollution. Traditional traffic signal systems operate on fixed time intervals and lack the ability to respond dynamically to real-time traffic conditions. In this study, an Internet of Things (IoT)-based intelligent traffic light detection and adaptive control system is proposed to improve traffic flow efficiency. The system integrates an ESP32 microcontroller with PIR motion sensors and sound sensors to detect vehicle presence and emergency signals at road intersections. Real-time traffic data are processed to dynamically adjust signal timing according to traffic density. Additionally, cloud-based monitoring using the Blynk platform enables remote supervision and data visualization of traffic conditions. The proposed system was implemented and evaluated through a prototype hardware setup, demonstrating improved responsiveness and efficient traffic management compared to conventional fixed-time systems. The results indicate that the adaptive traffic control mechanism significantly enhances traffic flow efficiency and provides a cost-effective solution for smart city transportation management.