Spatial panel model for examining airport relationships within multi-airport regions

For better airport planning and air traffic management, local airport authority in a multi-airport region (MAR) often needs to consider the impacts of competition and collaboration with nearby airports on its own airport traffic. This paper proposed a dynamic spatial panel regression model to test the regional effects on airports in a MAR. The proposed model is applied to four closely situated airports in the Pearl River Delta region (PRD), China, to analyze their interactions, identify the determining factors, and evaluate the impact of these factors on airport capacity. PRD is one of the most prosperous areas in Asia, and competition among the four airports has intensified, due, in part, to the rapid growth of Guangzhou airport and Shenzhen airport. Together with the fact that Hong Kong airport reached 98% of its runway capacity in 2016, it is of great interest to understand the interactions among the airports in this region. The findings show that airport degree, flight frequency, airport capacity utilization, income, population, GDP, and fuel price are significant factors affecting airport’s capacity. Furthermore, there is a spatially lagged effect in income and population, and a time-lagged effect in airport capacity, GDP, and fuel price.