E-commerce logistics management cargo volume prediction research: a cargo volume prediction system constructed based on LSTM models

Abstract

With the booming development of the Internet economy, the e-commerce market continues to expand, the importance of the e-commerce logistics industry has become increasingly prominent, and the continuous optimization of its level of service is imperative. Sorting link as the "main force" of e-commerce logistics, reasonable prediction of the amount of goods and analyze the transportation route can effectively reduce costs and improve efficiency. In this paper, the LSTM neural network model is used to predict the future operation of cargo volume for the historical cargo volume of the sorting center. By debugging the parameters and structure of the model for many times, it successfully predicts the cargo volume for the next 30 days and every hour, which provides effective support for the management of the logistics industry.