Academic Journal of Engineering and Technology Science, 2022, 5(4); doi: 10.25236/AJETS.2022.050404.
School of Economics and Management, Science and Technology College of Hubei University of Automotive Technology, Hubei, 442000, China
The outbreak of covid2019 coronavirus disease has a certain impact on the development of logistics industry. This paper aims to find out the typical factors affecting the logistics demand before and after covid2019, and predict the logistics demand in the next decade. This paper selects the total amount of social logistics from 2010 to 2020 as the index to measure the logistics demand, analyzes the factors affecting the total amount of social logistics, and establishes a logistics demand prediction model based on multiple linear regression. The results show that the demand of social logistics is most closely related to the total cost of social logistics and the investment in fixed assets of the whole society, but weakly related to the total import and export. At the same time, this paper also forecasts the logistics demand in the next decade to provide decision support for relevant departments.
Logistics demand; Multiple linear regression; Prediction model
Shangchen Yang. Analysis of Logistics Industry Demand Forecast in China. Academic Journal of Engineering and Technology Science (2022) Vol. 5, Issue 4: 18-22. https://doi.org/10.25236/AJETS.2022.050404.
 Wei Xu, Nan Yu. Application of grey BP neural network in port logistics demand analysis [J]. International Journal of Modelling in Operations Management, 2019, 7(3):
 Lu Liu, Yaning Zhao, Xinyan Dong, Yuanshuo Liu, Peng Qiao, Zhiyuan Xie, Cuihuan Ren, Fengchun Liu, Chunying Zhang. Research on Logistics Demand Forecasting Model Combining Time Series and Neural Network [J]. International Journal of Computational and Engineering, 2018, 3(3):
 Jiang Nan. Logistics demand forecast of Nanjing metropolitan area based on Grey GM (1,1) [J] Logistics technology, 2021, 40 (11): 44-47 + 53
 Xue Xianfan. Logistics demand combination forecasting model based on entropy method [J] China storage and transportation, 2021 (08): 135-137 DOI: 10.16301/j.cnki. cn12-1204/f.2021.08.063.
 Chen Min. Logistics demand forecast of Chengdu based on BP neural network [J] China storage and transportation, 2021 (05): 107-108 DOI: 10.16301/j.cnki. cn12-1204/f.2021.05.037.