Prediction of passenger traffic volume using ν-support vector regression optimized by chaos adaptive genetic algorithm
Aiming at the prediction of passenger traffic volume with small samples,multi-dimension and nonlinearity,ν-support vector regression（ν-SVR） is introduced to forecast passenger traffic volume.To seek the optimal forecast accuracy and generalization performance of ν-SVR,chaos adaptive genetic algorithm（CAGA） is used to optimize the parameter,which is based on chaos mapping and adaptive mechanism.Then,a new passenger traffic volume forecasting model of ν-SVR named by CAGA-ν-SVR is proposed.The model is applied to forecasting passenger traffic volume with data of 1978-2008.Compared with RBF neural network model,GA-SVR model and GA-ν-SVR model,it is concluded that CAGA-ν-SVR prediction model has higher prediction precision,and can effectively predict passenger traffic volume with less than 2.3% of mean absolute relative error.
国家科技期刊平台
NSTL
万方数据
维普
