Traffic Density Prediction System using Deep Learning

In major cities throughout the world, traffic congestion is still a major problem that negatively affects the efficiency of transportation system. Traffic congestion creates several issues that require a creative and adaptable strategy to solve. This work tackles the problem by accurately forecasting vehicle density by merging state-of-the-art deep learning models, specifically YOLO - v8 for real-time vehicles identification and Stacked Long Short-Term Memory (LSTM) models for forecasting traffic density. These methods are combined to create a powerful vehicle density prediction system. The integration of YOLOv8 with LSTM provides a practical response to the growing issues brought on by traffic congestion in cities. The simulation results shows that accuracy of YOLOv8 is 95.36, while average RMSE value of Stacked LSTM models is 0.591. The proposed model is compared with RNN model for vehicle density forecasting and the proposed model performs better than RNN in terms of accuracy.