원문정보
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
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