Forecasting airport passenger traffic: the case of Hong Kong International Airport

dc.contributor.authorTsui, Wai Hong Kan
dc.contributor.authorBalli, Hatice Ozer
dc.contributor.authorGower, Hamish
dc.date.accessioned2012-08-23T01:45:13Z
dc.date.available2012-08-23T01:45:13Z
dc.date.issued2011
dc.description.abstractHong Kong International Airport is one of the main gateways to Mainland China and the major aviation hub in Asia. An accurate airport traffic demand forecast allows for short and long-term planning and decision making regarding airport facilities and flight networks. This paper employs the Box-Jenkins Autoregressive Integrated Moving Average (ARIMA) methodology to build and estimate the univariate seasonal ARIMA model and the ARIMX model with explanatory variables for forecasting airport passenger traffic for Hong Kong, and projecting its future growth trend from 2011to 2015. Both fitted models are found to have the lower Mean Absolute Percentage Error (MAPE) figures, and then the models are used to obtain ex-post forecasts with accurate forecasting results. More importantly, both ARIMA models predict a growth in future airport passenger traffic at Hong Kong.en
dc.identifier.citationTsui Wai Hong Kan, Hatice Ozer BALLI & Hamish Gow (2011). Forecasting airport passenger traffic: the case of Hong Kong International Airport. Aviation Education and Research Proceedings, vol 2011, pp 54-62.en
dc.identifier.issn1176-0729
dc.identifier.urihttp://hdl.handle.net/10179/3717
dc.language.isoenen
dc.subjectHong Kong International Airporten
dc.subjectAirport passenger trafficen
dc.titleForecasting airport passenger traffic: the case of Hong Kong International Airporten
dc.typeArticleen
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