An Efficient Model for Real-Time Traffic Density Analysis and Management Using Visual Graph Networks

Real-time traffic management systems are needed to manage urbanization’s impact on traffic conditions. Traffic dynamics in cities are complex, and traditional signal timing methods and simple vehicle detection cannot handle them. The paper describes a method for improving urban traffic studies using Real-time Dense Analysis and Management using Visual Graph Networks (RDAMVGN) that utilizes deep learning techniques along with Visual Graph Networks based on visualizations. This study aims to develop a robust, dynamic, and accurate approach to traffic density analysis, vehicle classification, and dynamic signal control in order to achieve high accuracy in traffic flow analysis. The proposed RDAMVGN framework incorporates both a LACF-YOLO detection model and a faster region-based convolutional neural network (Faster RCNN) detection model that are of high accuracy and speed for fast moving vehicle movements. Transfer learning is applied to adapt learned features. This results in superior classification of vehicle objects amidst complex scenes and improved discrimination from non-vehicle categories. Traffic flow optimization is achieved through vehicle size and shape analysis using a mask RCNN, coupled with LSTM-based signal prediction for traffic pattern. The comparative analysis includes Fine-Tuned YOLOv8 with Faster RCNN, Fine-Tuned YOLOv3 with Faster RCNN, YOLOv5 with Faster RCNN, YOLOv3 with Faster RCNN, standalone Faster RCNN, YOLOv5, YOLOv3, Reinforcement Learning (RL), and Deep Reinforcement Learning (DRL) models, evaluated across precision, recall, accuracy, and pre-emption performance metrics. This RDAMVGN based deep learning demonstrably has superior performance across all core evaluation metrics. It achieved a high precision of 97.75 %, indicating accurate vehicle identification with minimal false positives. Its recall rate of 97.33 % reflects strong detection capability, minimizing missed vehicles. The overall accuracy stands at 96.69 %, indicating robust classification and localization. In traffic signal pre-emption tasks, the model maintains its lead with pre-emption precision of 96.25 %, pre-emption recall of 96.61 %, and pre-emption accuracy of 95.84 %, showcasing its reliability in real-time traffic prioritization scenarios.