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

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.

[1]  Yi Hu,et al.  Domestic air passenger traffic and economic growth in China: Evidence from heterogeneous panel models , 2015 .

[2]  Richard Weber,et al.  A Sequential Hybrid Forecasting System for Demand Prediction , 2007, MLDM.

[3]  Donald F. Specht,et al.  A general regression neural network , 1991, IEEE Trans. Neural Networks.

[4]  Shuo-Yan Chou,et al.  Air passenger demand forecasting and passenger terminal capacity expansion: A system dynamics framework , 2010, Expert Syst. Appl..

[5]  Shouyang Wang,et al.  Multiple dimensioned mining of financial fluctuation through radial basis function networks , 2014, Neural Computing and Applications.

[6]  Chaug-Ing Hsu,et al.  Application of Grey theory and multiobjective programming towards airline network design , 2000, Eur. J. Oper. Res..

[7]  Seraj Yousef Abed,et al.  An econometric analysis of international air travel demand in Saudi Arabia , 2001 .

[8]  Stephane Hess,et al.  Improving the quality of demand forecasts through cross nested logit: a stated choice case study of airport, airline and access mode choice , 2013 .

[9]  Ching-Wu Chu,et al.  A comparison of univariate methods for forecasting container throughput volumes , 2009, Math. Comput. Model..

[10]  J. M. Seixas,et al.  A multivariate neural forecasting modeling for air transport – Preprocessed by decomposition: A Brazilian application , 2009 .

[11]  Wang Sheng,et al.  Fruit fly optimization algorithm based fractional order fuzzy-PID controller for electronic throttle , 2013 .

[12]  Shouyang Wang,et al.  Application of multiscale analysis-based intelligent ensemble modeling on airport traffic forecast , 2015 .

[13]  Luis A. Gil-Alana,et al.  The US airline industry: persistence and breaks , 2013 .

[14]  Bin Hu,et al.  Forecasting Technology of National-Wide Civil Aviation Traffic , 2010 .

[15]  ChouShuo-Yan,et al.  Air passenger demand forecasting and passenger terminal capacity expansion , 2010 .

[16]  Chao Wu,et al.  Forecasting stock indices using radial basis function neural networks optimized by artificial fish swarm algorithm , 2011, Knowl. Based Syst..

[17]  M. A. Behrang,et al.  The Integration of Artificial Neural Networks and Particle Swarm Optimization to Forecast World Green Energy Consumption , 2012 .

[18]  Miguel de Carvalho,et al.  Tracking the US business cycle with a singular spectrum analysis , 2012 .

[19]  J. M. del Castillo,et al.  Improving trip forecasting models by means of the Box–Cox transformation , 2013 .

[20]  Robert I. McKay,et al.  Machine Learning for Predictive Management: Short and Long term Prediction of Phytoplankton Biomass using Genetic Algorithm Based Recurrent Neural Networks , 2012 .

[21]  Su-Mei Lin,et al.  Analysis of service satisfaction in web auction logistics service using a combination of Fruit fly optimization algorithm and general regression neural network , 2011, Neural Computing and Applications.

[22]  Søren L. Buhl,et al.  How (In)accurate Are Demand Forecasts in Public Works Projects?: The Case of Transportation , 2005, 1303.6654.

[23]  Shengyao Wang,et al.  A novel fruit fly optimization algorithm for the semiconductor final testing scheduling problem , 2014, Knowl. Based Syst..

[24]  R. Nasimi,et al.  An Improved Ant Colony Algorithm–Based ANN for Bottom Hole Pressure Prediction in Underbalanced Drilling , 2012 .

[25]  Robert Fildes,et al.  Evaluating the forecasting performance of econometric models of air passenger traffic flows using multiple error measures , 2011 .

[26]  L. Pazvakawambwa,et al.  Forecasting methods and applications. , 2013 .

[27]  Shouyang Wang,et al.  Ensemble ANNs-PSO-GA Approach for Day-ahead Stock E-exchange Prices Forecasting , 2014, Int. J. Comput. Intell. Syst..

[28]  Michael Y. Hu,et al.  Forecasting with artificial neural networks: The state of the art , 1997 .

[29]  Wen-Tsao Pan,et al.  A new Fruit Fly Optimization Algorithm: Taking the financial distress model as an example , 2012, Knowl. Based Syst..

[30]  Siem Jan Koopman,et al.  A non-Gaussian generalization of the Airline model for robust seasonal adjustment , 2006 .

[31]  Franz Rothlauf,et al.  Gravity models for airline passenger volume estimation , 2007 .

[32]  Kin Keung Lai,et al.  A neuro-fuzzy combination model based on singular spectrum analysis for air transport demand forecasting , 2014 .

[33]  Elton Fernandes,et al.  The causal relationship between GDP and domestic air passenger traffic in Brazil , 2010 .

[34]  Paul S. Biederman.  The Role of Forecasting at Trans World Airlines , 1993 .

[35]  E. Vercher,et al.  Holt–Winters Forecasting: An Alternative Formulation Applied to UK Air Passenger Data , 2007 .

[36]  M. Wolters,et al.  Comparative analysis of government forecasts for the Lisbon Airport , 2010 .

[37]  P. Young,et al.  Time series analysis, forecasting and control , 1972, IEEE Transactions on Automatic Control.

[38]  Andrew L. Rukhin,et al.  Analysis of Time Series Structure SSA and Related Techniques , 2002, Technometrics.

[39]  James G. MacKinnon,et al.  Numerical Distribution Functions for Unit Root and Cointegration Tests , 1996 .

[40]  Shouyang Wang,et al.  A multiscale modeling approach incorporating ARIMA and anns for financial market volatility forecasting , 2014, J. Syst. Sci. Complex..

[41]  G. P. King,et al.  Extracting qualitative dynamics from experimental data , 1986 .

[42]  Steven C. Wheelwright,et al.  Forecasting: Methods and Applications, 3rd Ed , 1997 .

[43]  George E. P. Box,et al.  Time Series Analysis: Forecasting and Control , 1977 .