Regional Logistics Demand Prediction: A Long Short-Term Memory Network Method

Abstract

With the growth of e-commerce and the recurrence of the novel coronavirus pneumonia outbreak, the global logistics industry has been deeply affected. People are forced to shop online, which leads to a surge in logistics needs. Conversely, the novel coronavirus can also be transmitted through goods, so there are some security risks. Thus, in the post-epidemic era, the analysis of regional logistics needs can serve as a foundation for logistics planning and policy formation in the region, and it is critical to find a logistics needs forecasting index system and a effective method to effectively exploit the logistics demand information in recent years. In this paper, we use the freight volume to assess the logistics needs, and the Long short-term memory (LSTM) network to predict the regional logistics needs based on time series and impact factors. For the first time, the Changsha logistics needs prediction index system is built in terms of e-commerce and the post-epidemic era and compared with some well-known methods such as Grey Model (1,1), linear regression model, and Back Propagation neural network. The findings show that the LSTM network has the smallest prediction errors, and the logistics needs are not affected by the epidemic. Therefore, the authors suggest that the government and businesses pay more attention to regional logistics needs forecasting, choosing scientific prediction methods.