An Edge-Deployed Real-Time Adaptive Traffic Light Control System Using YOLO-Based Vehicle Detection and PCE-Aware Density Estimation

This paper presents an edge-deployable, real-time adaptive traffic light control system designed for complex and undisciplined urban traffic environments. The system integrates computer vision and Internet of Things (IoT) technologies to enable smart traffic management. A robust dataset was developed to capture diverse traffic conditions, including occlusions and unstructured vehicle movement. A lightweight vehicle detection model, based on (You Only Look Once) YOLO and deployed on a Jetson Xavier NX-powered roadside edge node (RSEN), enables real-time traffic monitoring with a mean Average Precision (mAP) of 90% and 74 frames per second (FPS). To improve traffic flow representation, we introduce a traffic density estimation method that incorporates Passenger Car Equivalent (PCE) factors, considering the varying impact of different vehicle types. The estimated density is used by an adaptive traffic light control (ATLC) algorithm, deployed on a resource-constrained Portenta H7 board, which dynamically adjusts signal timings using real-time data, starvation prevention, and tie-breaking mechanisms. The proposed system reduces traffic congestion at intersections by up to 33% and waiting times by 23% compared to traditional fixed-time controls. The proposed framework is cost-effective, scalable, and fully operational on edge devices, offering a practical solution for real-world smart city deployments.