Abstract
In a context of debate over the future of the US Federal Aviation Administration's (FAA) funding model, this paper revisits the current system of airport classification used for the allocation of public funding for capacity developments. Previous papers have already addressed the limitations of the FAA's uni-dimensional method, and proposed new approaches that take into account the two dimensions of “hubbing” activity, i.e., traffic generation and connectivity. However, these studies are biased by the lack of detailed demand data on international connections. Using an MIDT dataset comprising a sample of domestic and international markets served by US airports during the first quarter of 2013, this paper aims at providing a full picture on the pitfalls of the existing FAA method, as well as addressing the impact of international connectivity in characterising the airports' hubbing profiles. Hierarchical clustering is used to provide alternative criteria for hub classification within the context of US National Plan of Integrated Airport Systems (NPIAS). This new typology of primary US airports can help to optimize AIP funding by allowing for further differentiation in the FAA allocation criteria.
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