Year: 2025
Type: article
Abstract: In urban areas, unmanaged traffic congestion leads to delays, fuel wastage, and increased pollution. This paper proposes an AI-based traffic control system that dynamically adjusts traffic signal durations based on real-time vehicle density. The system is implemented as a mini working model, and for this purpose, a YOLO object detection model is trained specifically on images of toy vehicles to detect and count them from a USB webcam feed. The processed data is used to determine traffic density, and the ESP32 microcontroller is interfaced to control signal lights accordingly. The system achieves a cost-effective and efficient solution for intelligent traffic management, especially in a scaled-down prototype model using toy vehicles. Key Words: YOLO, ESP32, Dynamic Traffic Control, Object Detection, Smart City, Real-Time Detection
DOI: 10.55041/ijsrem44810
Author: S Anupama
Language: English
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W4411115496