Hong 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.
Tsui 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.