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A model to forecast airport-level General Aviation demand

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  • Li, Tao
  • Trani, Antonio A.

Abstract

General Aviation (GA) demand forecast plays an important role in aviation management, planning and policy making. The objective of this paper is to develop an airport-level GA demand forecast model. The GA demand at an airport is modeled as a function of social-economic and demographic factors, the availability of supply factors, the competition from the commercial aviation, the number of based aircraft, and the presence of a flight school. Our models suggest that the relative fuel price – fuel price compared with personal income – is a significant determinant of airport level GA demand. The elasticity of itinerant and local GA demand with respect to the relative fuel price is −0.43 and −0.52, respectively. Our results are compared with those reported in other studies. Furthermore, we made projections of GA demand for the airports in the Terminal Area Forecast (TAF) using three fuel price scenarios from the Energy Information Administration. Our projections under the “business-as-usual†fuel price scenario are close to those in the TAF. Our models could prove useful, for example, for the Federal Aviation Administration and airport planners to prepare airport-level GA demand forecast.

Suggested Citation

  • Li, Tao & Trani, Antonio A., 2014. "A model to forecast airport-level General Aviation demand," Journal of Air Transport Management, Elsevier, vol. 40(C), pages 192-206.
    • Handle: RePEc:eee:jaitra:v:40:y:2014:i:c:p:192-206
    DOI: 10.1016/j.jairtraman.2014.07.003
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    References listed on IDEAS

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    Cited by:

    1. Shaban, I.A. & Wang, Z.X. & Chan, F.T.S. & Chung, S.H. & Eltoukhy, A.E.E. & Qu, T., 2019. "Price setting for extra-baggage service for a combination carrier using the newsvendor setup," Journal of Air Transport Management, Elsevier, vol. 78(C), pages 1-14.
    2. Tao Li, 2017. "A Demand Estimator Based on a Nested Logit Model," Transportation Science, INFORMS, vol. 51(3), pages 918-930, August.
    3. Li, Max Z. & Ryerson, Megan S., 2019. "Reviewing the DATAS of aviation research data: Diversity, availability, tractability, applicability, and sources," Journal of Air Transport Management, Elsevier, vol. 75(C), pages 111-130.
    4. Li, Tao & Wan, Yan, 2019. "Estimating the geographic distribution of originating air travel demand using a bi-level optimization model," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 131(C), pages 267-291.

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