Modeling and Simulation Research for Air Traffic Forecasting
Xin Wang · Jisuanji fangzhen · 2011
Air traffic flow changes with nonlinear change characteristics,the traditional forecasting methods cannot describe the change rule and prediction accuracy is low.In order to improve the air traffic flow predictive accuracy,an air traffic flow prediction model is proposed based on grey prediction and support vector machine(SVM).Firstlly,the model uses grey model to predict the air traffic flow linear laws,and uses support vector machine to predict the grey model's residual sequence,reflecting its nonlinear variation.Lastly,two results are summariede to get air traffic flow prediction results.Simulation results show that prediction accuracy is better than single prediction results.The model makes full use of the advantages of two models,and can accurately describe air traffic flow change rule and overcome the single model defects.It is more suitable for air traffic flow prediction.