Oscillations extracting for the management of passenger flows in the airport of Hong Kong

Abstract

Managing air passenger traffic flows is important in investing and operation of airports. However, it is extremely difficult for traditional methods to analyse passenger traffic in both the short and medium terms because of the oscillation and irregularity inherent in air passenger traffic flows dynamics. In this study, we design a hybrid oscillations analysis approach. The proposed method decomposes time series into different scales, making it useful in revealing structural breaks and volatility clusters, and identifying dynamic properties of a process at specific timescales. A case study of Hong Kong airport demonstrates and validates the feasibility of applying the proposed models. Empirical results have confirmed that the proposed model is superior to other competing models and can provide high flexibility in decision-making.

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