Bayesian Neural Network-Based Demand Forecasting for Express Transportation

Abstract

The rapid development of e-commerce in recent years has driven the growth of the logistics service industry, which in turn has led to a significant increase in express delivery volume. Predicting express delivery volume accurately and in advance can help companies allocate various resources reasonably and provide the basis for predicting express delivery demand. To predict the specific transport volume of XX Express Company's logistics routes on April 28th and 29th, 2023, this article builds two Bayesian prediction models based on the company's historical transportation data as the training set, one for predicting the opening of logistics routes and the other for predicting express delivery quantity. This approach reduces the impact of inaccurate predictions due to logistics routes not being opened properly and improves the accuracy of the prediction model. It provides decision support for optimizing resource layout for express delivery companies.