Deep Learning –Based Traffic Density Estimation And Analysis

Congestion on roads, air pollution from automobile emissions, and a rising rate of accidents are only a few of the critical problems with which metropolitan areas are faced because of the intensifying automobile population caused by urban growth. These issues become particularly evident in developing countries, where it remains challenging to practice effective traffic tracking.This work presents a complex traffic surveillance system based on aerial drone videos and advanced computer vision algorithms.YOLOv11 and Graph Neural Networks (GNN) are the core algorithms employed to assess real-time traffic images captured by high-definition drone cameras. These algorithms reach 97% and 91% recognition accuracy, respectively. In addition, YOLOv8 and ResNet50 were utilized, with 90% and 85% recognition accuracies, respectively. In addition, traffic flow patterns can be predicted precisely with time series forecasting models such as LSTM and GRU. The GRU model had an RMSE accuracy of 0.943%, but the LSTM model had an MSE of 20.98%. Real-time traffic data analysis, congestion detection, and traffic monitoring are all enhanced by this integrated system.For making decisions faster to manage urban traffic, future upgrades will focus on improving real-time traffic forecasting through edge computing and adding federated learning for edge data processing.