MATLAB BP Neural Network Program for Highway Passenger Volume Prediction

This document discusses the methodology for predicting highway passenger volume using a MATLAB Backpropagation (BP) neural network program. This neural network implementation serves as a powerful tool for accurately forecasting future highway traffic volumes. The program incorporates multi-layer perceptron architecture with sigmoid activation functions and gradient descent optimization for training. Through this implementation, we can analyze multiple influencing factors including temporal patterns, weather conditions, holiday schedules, and economic indicators to predict traffic conditions on highways. The algorithm processes input data through forward propagation, calculates errors using mean squared error (MSE), and adjusts network weights through backpropagation with momentum for stable convergence. This approach enables traffic planners and government agencies to better understand and manage traffic flow patterns, facilitating data-driven decision-making for infrastructure development and traffic control measures. By utilizing this neural network implementation with features like adaptive learning rates and regularization techniques, we significantly improve the accuracy and efficiency of traffic volume predictions, thereby adding substantial value to transportation management and urban planning domains.