基于组合预测模型的航空货运吞吐量预测研究

目前对于机场货运吞吐量的预测方法不尽合理,导致预测与实际情况差别较大,为此本文提出将内在影响因素融入组合预测模型,从而对航空货物运输进行预测,使得预测结果更加接近实际情况.本文以香港国际机场为背景,运用Shapley值法将BP神经网络和灰色理论预测模型进行组合,对其航空货运吞吐量进行了实证研究,并将此预测方法推广应用于其他机场的预测.

Research on the Air Cargo Forecast Based On Combination Forecast Model

The prediction methods of freight handle used currently have some defects and lead to the result of prediction with big deviation to the real situation.For according with the real situation,a new algorithm of combination forecasting model with a many of intrinsic factors is proposed to predict air cargo.Under the background of Hong Kong International Airport,this paper uses the Shapley method to combine BP neural network and gray theory as a combination forecasting model,analyzing the airline transportation scale with real example.Finally,this approach is applied to predicting of other airports.