Air Traffic Flow Combinational Forecast Based on Double Gravity Model and Artificial Neural Network
Linyuan Huang · Journal of Southwest Jiaotong University · 2009
The four stage method and a double gravity model were used to forecast the OD(origin-destination) distribution of air traffic flow in the whole airspace of China.In view of the randomicity and periodicity of historical data of air traffic flow,a GM-GRNN(gray model and generalized regression neural network) combinational model was built to obtain the forecast results.The forecast results were then analyzed by Markov chain forecast model.A case study shows that,compared with the statistical data of air traffic flow OD distribution in north China airspace,the forecast results by the proposed combinational model are more precise and credible than the regression analysis and the traditional GRNN model.