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Regulatory airport classification in the US: The role of international markets

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  • Suau-Sanchez, Pere
  • Voltes-Dorta, Augusto
  • Rodríguez-Déniz, Héctor

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.

Suggested Citation

  • Suau-Sanchez, Pere & Voltes-Dorta, Augusto & Rodríguez-Déniz, Héctor, 2015. "Regulatory airport classification in the US: The role of international markets," Transport Policy, Elsevier, vol. 37(C), pages 157-166.
  • Handle: RePEc:eee:trapol:v:37:y:2015:i:c:p:157-166
    DOI: 10.1016/j.tranpol.2014.11.003
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    References listed on IDEAS

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    Cited by:

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    2. Gao, Yi, 2021. "What is the busiest time at an airport? Clustering U.S. hub airports based on passenger movements," Journal of Transport Geography, Elsevier, vol. 90(C).
    3. Voltes-Dorta, Augusto & Rodríguez-Déniz, Héctor & Suau-Sanchez, Pere, 2017. "Vulnerability of the European air transport network to major airport closures from the perspective of passenger delays: Ranking the most critical airports," Transportation Research Part A: Policy and Practice, Elsevier, vol. 96(C), pages 119-145.
    4. Cheung, Tommy K.Y. & Wong, Collin W.H. & Zhang, Anming, 2020. "The evolution of aviation network: Global airport connectivity index 2006–2016," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 133(C).
    5. Voltes-Dorta, Augusto & Rodríguez-Déniz, Héctor & Suau-Sanchez, Pere, 2017. "Passenger recovery after an airport closure at tourist destinations: A case study of Palma de Mallorca airport," Tourism Management, Elsevier, vol. 59(C), pages 449-466.
    6. Zeigler, Patrick & Pagliari, Romano & Suau-Sanchez, Pere & Malighetti, Paolo & Redondi, Renato, 2017. "Low-cost carrier entry at small European airports: Low-cost carrier effects on network connectivity and self-transfer potential," Journal of Transport Geography, Elsevier, vol. 60(C), pages 68-79.
    7. Suau-Sanchez, Pere & Voltes-Dorta, Augusto & Rodríguez-Déniz, Héctor, 2016. "Measuring the potential for self-connectivity in global air transport markets: Implications for airports and airlines," Journal of Transport Geography, Elsevier, vol. 57(C), pages 70-82.
    8. Singh, D.P. & Dalei, Narendra N. & Raju, T. Bangar, 2016. "Forecasting investment and capacity addition in Indian airport infrastructure: Analysis from post-privatization and post-economic regulation era," Journal of Air Transport Management, Elsevier, vol. 53(C), pages 218-225.

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