 Big data prediction method of traffic logistics demands based on regional differencesRongting Sun and Yiqun Guo International Journal of Information Technology and Management, 2021, vol. 20, issue 3, 221-233 Abstract: Aiming at the optimisation effect of traditional methods on logistics transportation, and the inaccurate prediction of logistics demand, this paper proposes a big data forecasting method for traffic logistics demand based on regional differences. Based on the regional differences, a linear statistical programming model for the prior data of traffic logistics demand data is established. On this basis, the association rule feature decomposition and average mutual information analysis are carried out for the traffic logistics demand big data. The BP fuzzy decision classification model is adopted for feature information clustering and information fusion processing of traffic logistics demand big data to optimise the big data prediction model. The simulation results show that the model has higher accuracy and better global convergence in the big data forecast of traffic logistics demand, which improves the overall forecasting ability, and the forecasting time is reduced by 12.8% compared with the traditional method. Keywords: regional difference; traffic logistics; big data prediction; fuzzy decision; linear planning model. (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:ids:ijitma:v:20:y:2021:i:3:p:221-233 Access Statistics for this article More articles in International Journal of Information Technology and Management from Inderscience Enterprises Ltd |
Is your work missing from RePEc? Here is how to contribute.
Questions or problems? Check the EconPapers FAQ or send mail to .
EconPapers is hosted by the
School of Business at Örebro University.