Prediction of Railway Passenger Traffic Volume by means of LS-SVM

摘要

Based on Least Squares Support Vector Machine (LS-SVM), a method for the prediction of railway passenger traffic volume is proposed. The railway passenger traffic volume from 1985 to 2002, provided by National Bureau of Statistics of China, is employed as total data set. The normalized passenger volume from 1985 to 1999 is used as training data set to establish LS-SVM model, while the normalized volume from 1999 to 2002 is utilized as testing data set to carry out prediction. LS-SVM is applied to establish prediction model. The prediction results by LS-SVM model are compared with those by BP neural network method. The results show that LS-SVM outperforms BP neural network in the prediction of railway passenger traffic volume.