Short-Term Traffic Flow Prediction Based on BP Neural Network

This research project focuses on short-term traffic flow prediction using Backpropagation (BP) neural networks. By utilizing MATLAB's neural network toolbox, we implement an efficient system for traffic flow forecasting and analysis. The implementation involves data preprocessing, network architecture design with hidden layers, and training using gradient descent algorithms. This research holds significant importance for traffic management and urban planning, providing valuable insights for addressing urban traffic congestion issues. Through this code, we employ advanced neural network techniques to predict traffic volume, enhancing the accuracy and efficiency of traffic management systems. The project aims to provide reliable data and tools for future traffic planning and decision-making, ultimately improving the operation and sustainability of urban transportation systems. Key features include customizable input parameters, performance evaluation metrics, and visualization of prediction results.