A hybrid Model of Neural Network and Grey Theory for Air Traffic Passenger Volume Forecasting

摘要

Chinese air traffic passenger volumes have experienced phenomenal growth during the past years. The air traffic volume prediction plays a key role in air traffic flow management system. This paper develops a hybrid model of Neural and Grey Theory for air traffic passenger volume forecasting. The Grey theory is adopted to fit the air traffic data patterns and make the data a higher regularity, and Radical basis function is combined to raise the forecasting accuracy. The model is tested with the Chinese civil aviation passenger volume data from 1998 to 2007 and the result shows that the model is feasible for practical implementations.