Construction of passenger flow prediction model for new routes in civil aviation advance flight planning basis
With the development of economy,airlines have opened up brand new routes to satisfy the public demand.However,the existing forecasting methods can not meet the high requirements of these new routes to determine the passenger flow.In view of this,the study firstly analyzes and summarizes the influencing factors of passenger flow,and then selects the Support Vector Regression(SVR)algorithm as the basis,and introduces the Gaussian Radial Basis function(RBF)core function for optimization.The experimental results show that the SVR-RBF model has the highest passenger flow prediction accuracy of 89.7%,the deviation is lower than 53.4%,and the difference between the predicted value and the real passenger flow value of the SVR-RBF model is small compared with the same type of passenger flow prediction model.In summary,SVR-RBF model can better predict the passenger flow of new routes,can help airlines to meet the demand of the mass market,and provides an effective theoretical support for the development of civil aviation.
New route openingPassenger flow forecastingSupport vector regressionRBFCore function
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