Regional Logistics Demand Prediction Using BP Neural Network Based on Particle Swarm Optimization

Abstract

Accurate regional logistics demand prediction is an indispensable part of a scientific and reasonable logistics construction system. This article mainly uses machine learning methods to screen features, select effective features, and then uses intelligent algorithms to optimize the BP neural network. The optimized model is trained and predicted for the future, optimizing research on regional logistics demand prediction, and providing effective suggestions for the development of the logistics industry. This article first uses the random forest algorithm to select features, eliminate redundant features, and then uses particle swarm optimization algorithm to optimize the parameters of the BP neural network to improve model performance. Finally, grey prediction is used to construct a prediction dataset, and the future trend of regional logistics demand is predicted, based on which suggestions are proposed. The results show that the optimized combination model proposed in this article can effectively improve prediction accuracy and make an effective contribution to the development of regional logistics.