Quantity Modeling and Application of Multivariable Correlation Analysis

Abstract

Abstract-This study focuses on quantitative correlation problem of four Railway Parcel traffic parameters: Number of Initial trains (NIT), GDP of cities, Number of Parcel Traffic Agencies (NPTA) and Number of Parcel traffic Nodes (NPTN). It can be seen as a multivariable systems that called Multiple-Input Single-Output(MISO). Then ANN is used in to resolve the multivariable Correlation Analysis problems in China Railway Parcel forecast. Based on Artificial Neural Networks (ANN), the prediction of China Railway Parcel Traffic Volume is modeling. The model can effectively solve the variable multiple correlation problem. Good performance is demonstrated when Application proves the accuracy of the model and its contribution.