Study on Composite Forecasting Model of Air Passenger Capacity Based on Air Partition
Firstly, an air passenger capacity investigation at the capital international airport is made, and a composite forecasting model based on total air passenger capacity is established, in which multiple regression and ARIMA model are parallel connection and their forecast results are series connection with BP neural network. Secondly, according to the average growth rate of air passenger capacity, all airlines are divided into 5 subareas, and the series connection model of ARIMA and BP neural network is established. Finally, short-term air passenger capacity at the capital international airport is forecasted by the composite models, and analyzed results show that the model based on air partition is more precise than the model based on total air passenger capacity, which is a kind of viable and practicable air passenger forecasting model.
air passengr capacity air partition ARIMA neural network composite forecasting model
Xing-qiang Zhang Xue Yang Shi-qing Dong
MOE Key Laboratory for Transportation Complex Systems Theory and Technology Beijing Jiaotong Univers School of Traffic and Transportation Beijing Jiaotong University, 100044 Beijing, China
国际会议
太原
英文
66-69
2010-10-22（万方平台首次上网日期，不代表论文的发表时间）