Logistics demand forecast in Anhui Province based on combination forecasting model of BP neural network and second exponential smoothing method
XU Jian
GUI Haixia
Abstract：In order to accurately predict the logistics demand in Anhui Province,the regional gross domestic product,the output value of the primary,secondary,and tertiary industries,the total retail sales of social consumer goods,the fixed asset investment,and the per capita consumer expenditure of Anhui Province are selected as the evaluation indicators for Anhui Province's logistics demand from four aspects:economic development,output structure,regional trade,and consumption level.The freight volume of Anhui Province is used as the output indicator of logistics demand scale.The grey correlation analysis is adopted to calculate the correlation between the evaluation indicators of logistics demand and the logistics demand scale,and to judge the rationality of the evaluation indicators.By combining the back propagation(BP)neural network prediction model with the second exponential smoothing method prediction model using the Shapley value method,the logistics demand of Anhui Province from 2017 to 2021 is predicted.The results show that the average relative errors of the BP neural network prediction model,the second exponential smoothing prediction model,and their combination prediction model are 4.58％,6.70％,and 3.99％respectively,with the combination prediction model having the smallest average relative error.The combination prediction model predicts the logistics demand of Anhui Province from 2022 to 2024 to be 405 004.96 thousand tons,407 142.09 thousand tons,and 409108.95 thousand tons respectively.The freight volume of Anhui Province shows a continuous growth trend,but the growth rate is decreasing.Anhui Province should accelerate the transfer speed from traditional logistics to intelligent logistics,expand domestic demand,strengthen the connection between logistics hub cities,accelerate the pace of regional integration development,and ensure the high-quality development of logistics.
Keywords：combination prediction modelBP neural network modelsecond exponential smoothing method modellogistics demandforecost
Publication Date：2024-09-30
Online Publishing Date：2026-05-22(First online date of this platform, not the publication date of the document)
Pages：7( 39-45 )