Prediction of urban passenger transport based-on wavelet SVM with quantum-inspired evolutionary algorithm

Abstract

Based on least squares wavelet support vector machines (LS-WSVM) with quantum-inspired evolutionary algorithm (QEA), the prediction model of urban passenger transport is proposed , that can provide the theoretical foundation of forecasting passenger volume of urban transport accurately. The prediction model of urban passenger transport is established by using LS-WSVM, whose regularization parameter and kernel parameter are adjusted using quantum-inspired evolutionary algorithm. QEA with quantum chromosome and quantum mutation has better global search capacity. The parameters of LS-WSVM can be adjusted using quantum-inspired evolutionary optimization. Combining with the data of the urban volume of passenger transport of Xipsilaan over years, the prediction model of urban passenger transport is validated, the simulation results indicate that the prediction model is effective, and based on LS-WSVM has more improvement than LS-SVM with Gaussian kernel in predicting precision, and then the improved LS-WSVM with QEA is efficient than with cross-validation method for tuning parameters.