Intelligent Traffic Flow Prediction Using Deep Learning Techniques

Abstract

The rapid growth of urban populations and increasing traffic congestion have prompted the need for efficient traffic management systems. Accurate traffic flow forecasting is crucial for optimizing transportation infrastructure, reducing congestion, and enhancing safety. This thesis explores the use of deep learning techniques to predict real-time traffic flow, aiming to develop an intelligent system capable of providing timely and accurate traffic predictions. The study leverages several deep learning models, including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Long Short-Term Memory (LSTM) networks, to forecast traffic conditions based on historical traffic data. Through experimentation with multiple datasets, including urban traffic data and public traffic flow databases, the research investigates the effectiveness of these models in capturing complex traffic patterns. Key performance metrics such as Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and R² are used to evaluate the models’ prediction accuracy. The results demonstrate that deep learning models, particularly hybrid CNN-LSTM models, outperform traditional forecasting methods, offering improved accuracy and adaptability in dynamic traffic environments. Challenges such as data quality, real-time prediction constraints, and the influence of external factors like weather and events are also addressed. The findings suggest that deep learning has the potential to revolutionize traffic management by providing more accurate and timely forecasts, thereby aiding in the development of smarter, more efficient transportation systems. Future work includes exploring multi-modal data integration and real-time prediction implementation to further enhance the capabilities of traffic forecasting systems.