A Self-Adaptive Traffic Signal System Integrating Real-Time Vehicle Detection and License Plate Recognition for Enhanced Traffic Management

Traffic management systems play a crucial role in smart cities, especially because increasing urban populations lead to higher traffic volumes on roads. This results in increased congestion at intersections, causing delays and traffic violations. This paper proposes an adaptive traffic control and optimization system that dynamically adjusts signal timings in response to real-time traffic situations and volumes by applying machine learning algorithms to images captured through video surveillance cameras. This system is also able to capture the details of vehicles violating signals, which would be helpful for enforcing traffic rules. Benefiting from advancements in computer vision techniques, we deployed a novel real-time object detection model called YOLOv11 in order to detect vehicles and adjust the duration of green signals. Our system used Tesseract OCR for extracting license plate information, thus ensuring robust traffic monitoring and enforcement. A web-based real-time digital twin complemented the system by visualizing traffic volume and signal timings for the monitoring and optimization of traffic flow. Experimental results demonstrated that YOLOv11 achieved a better overall accuracy, namely 95.1%, and efficiency compared to previous models. The proposed solution reduces congestion and improves traffic flow across intersections while offering a scalable and cost-effective approach for smart traffic and lowering greenhouse gas emissions at the same time.