Quantity Modeling and Application of Multivariable Correlation Analysis

Author

Guoqiang, Cai ; Limin, Jia ; Jianwei, Yang ; Haibo, Liu ; Xi, Li

Author_Institution

State Key Lab. of Rail Traffic Control & Safety, Beijing Jiaotong Univ., Beijing, China

fYear

2009

fDate

24-26 Nov. 2009

Firstpage

1670

Lastpage

1673

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.

Keywords

correlation methods; multivariable systems; neural nets; railway engineering; traffic engineering computing; MISO system; artificial neural networks; multiple-input single-output system; multivariable correlation analysis; multivariable systems; quantity modeling; railway parcel traffic; Artificial neural networks; Laboratories; Neural networks; Predictive control; Predictive models; Productivity; Rail transportation; Railway safety; Telecommunication traffic; Traffic control; Artificial neural network(ANN); China Railway Parcel; Multivariable Correlation; Traffic Estimate model;

fLanguage

English

Publisher

ieee

Conference_Titel

Computer Sciences and Convergence Information Technology, 2009. ICCIT '09. Fourth International Conference on

Conference_Location

Seoul

Print_ISBN

978-1-4244-5244-6

Electronic_ISBN

978-0-7695-3896-9

Type

conf

DOI

10.1109/ICCIT.2009.320

Filename

5369306