Railway passenger and freight prediction based on RBF neural network theory

Based on characteristic of RBF neural network theory to analyze the chaos of nonlinear dynamic systems,railway passenger and freight volume time series were analyzed. Specifically,on the basis of Takens phase space reconstruction,firstly mutual information method was used to calculate embedded time-delay and false neighbor method was utilized to calculate embedded dimension. Then G-P method and maximal Lyapunov index method were adopted to identify the chaos of railway passenger and freight volume time series. The next step,the prediction course of railway passenger and freight volume was analyzed using the learning algorithm and the identification principle. Finally RBF neural network theory was used to conduct prediction on railway passenger and freight volume from January 1st 1999 to August 27 th 2012 with a total of 4988 days. Thereinto the prediction error was examined and the prediction result was analyzed. The result shows that the predicted data using RBF neural network theory is in accordance with the real data. Therefore RBF neural network theory has extensive and practical value in railway passenger and freight volume time series prediction.