MATLAB Implementation of Freight Volume Prediction Using BP Neural Network

This article presents a MATLAB implementation of freight volume prediction using BP (Backpropagation) Neural Network. In modern logistics, freight volume forecasting is crucial for enabling businesses to make informed decisions, such as transportation route planning. By leveraging historical data through BP neural networks, we can predict future freight volumes with improved accuracy. The implementation covers key stages including data normalization using MATLAB's mapminmax function, neural network architecture design with the feedforwardnet function, and model training via the Levenberg-Marquardt algorithm (trainlm). We provide practical code examples demonstrating how to configure hidden layer neurons, set training parameters (net.trainParam), and evaluate performance metrics like Mean Squared Error (MSE). The article also discusses optimization techniques including early stopping to prevent overfitting and learning rate adjustment using trainbr for Bayesian regularization. Through this guide, readers will gain hands-on experience in implementing BP neural network-based forecasting solutions, contributing to both academic research and practical applications in logistics.