Freight volume prediction of Fujian Province based on Informer neural network
YAN Xuebo
ZHONG Kaibin
Abstract：To accurately predict freight volume,this study examines the freight volume in Fujian Province by selecting the main influencing factors of freight volume from four aspects:industrial structure level,economic development level,logistics development level,and human factors.A dataset is constructed using data on freight volume and its influencing factors in Fujian Province from 1978 to 2022.Based on the Informer neural network,a freight volume prediction model is constructed and trained using cross-validation methods.Simultaneously,the study employs long short-term memory(LSTM)neural networks and Transformer neural networks to predict the freight volume in Fujian Province,comparing the prediction accuracies of the three models.The results show that the average percentage error of the test set for the Informer neural network model is 3.75％,which is smaller than that of the LSTM and Transformer neural network models,at 4.45％and 4.38％,respectively,which indicates that the Informer neural network model provides more accurate predictions.The Informer neural network model predicts that the freight volume in Fujian Province in 2023 will be 1 842.89 million tons,an increase of 8.9％over 2022.The freight volume in Fujian Province is increasing year by year,and there should be continuous improvements to the logistics distribution mechanism and enhancements in transportation efficiency to meet the logistics demands of Fujian Province and the surrounding regions.
Keywords：freight volumepredictionInformer neural networkcross-validation
Publication Date：2025-03-29
Online Publishing Date：2026-05-22(First online date of this platform, not the publication date of the document)
Pages：8( 16-23 )