Earticle

초록

영어

In this article, aiming at complex prediction problems of the rail transit passenger flow and prediction problems combined with the actual situation of rail transit in Beijing. We will propose a fusion model of passenger flow for predicting. It can improve the prediction accuracy for passenger flow forecasting. We use the partial least squares regression method to solve multicollinearity between the dependent variable. The method of principal component analysis can rescreen the all the factors which are affect the passenger flow. To extract comprehensive variable this has the best ability to explain the passenger flow from all of the information, in order to solve the relevance of statistics, noise and information redundancy. The nonlinear prediction model which is between comprehensive variable and passenger flow will be established. Finally, through the passenger flow forecasting of exchange station of Beijing Metro Line 1 to verify the effectiveness of the method.

목차

Abstract
 1. Introduction
 2. The Basic Model of SVR
 3. The Types and Selection of Kernel Function
  3.1 The types of Kernel function
  3.2. The Performance of Kernel Function
  3.3 Selection of Kernel function
 4. The PLS-SVR Model with Mixed Kernel Function
  4.1. PLS Regression Model
  4.2. PLS-SVR Model
 5. The Simulation and the Conclusion
 Acknowledgements
 References

키워드

저자

• Huijuan Zhou [ Beijing Key Laboratory of Urban Intelligent Traffic Control Technology, North China University of Technology No. 5 Jinuanzhuang Road, Shijingshan District, Beijing, China 100144 ]
• Yong Qin [ State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University No. 3 Shangyuancun, Haidian District, Beijing, China, 100044 ]
• Yinghong Li [ Beijing Key Laboratory of Urban Intelligent Traffic Control Technology, North China University of Technology No. 5 Jinuanzhuang Road, Shijingshan District, Beijing, China 100144 ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of u- and e- Service, Science and Technology

• 간기

  격월간

• pISSN

  2005-4246

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605