A Model to Forecast Airport Level General Aviation Demand 2014 Journal of Air Transport Management

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A model to forecast airport-level General Aviation demandTao Li*, Antonio A. TraniThe Charles E. Via, Jr. Department of Civil and Environmental Engineering, Virginia Polytechnic Institute and State University, 200 Patton Hall, Blacksburg,VA 24060, USAarti cle i nfoArticle history:Available online 2 August 2014Keywords:General AviationAirport-level GA demand forecastItinerant operationsLocal operationsRelative fuel priceImpact of fuel priceabstractGeneral Aviation (GA) demand forecast plays an important role in aviation management, planning andpolicy making. The objective of this paper is to develop an airport-level GA demand forecast model. TheGAdemandatanairportismodeledasafunctionof social-economicanddemographicfactors, theavailability of supply factors, the competition from the commercial aviation, the number of based aircraft,and the presence of aight school. Our models suggest that the relative fuel price e fuel price comparedwith personal incomee is a signicant determinant of airport level GA demand. The elasticity of itin-erant and local GA demand with respect to the relative fuel price is 0.43 and 0.52, respectively. Ourresultsarecomparedwiththosereportedinotherstudies. Furthermore, wemadeprojectionsofGAdemand for the airports in the Terminal Area Forecast (TAF) using three fuel price scenariosfrom theEnergy Information Administration. Our projections under the business-as-usual fuel price scenario areclose to those in the TAF. Our models could prove useful, for example, for the Federal Aviation Admin-istration and airport planners to prepare airport-level GA demand forecast. 2014 Elsevier Ltd. All rights reserved.1. IntroductionGeneral Aviation(GA)istheoperationof civilianaircraft forpurposes other than commercial passenger or cargo transport. Intheliterature,asignicantamount ofeffort hasbeen devotedtoforecasting GA demand. This paper is intended as a contribution tothis topic. GA demand forecast plays an important role in aviationmanagement, planning and policy making. For example, the fore-cast of GAactivitiesisonefactor usedbytheFederal AviationAdministration(FAA)toconductbenet-costanalysesassociatedwith airport development (GRA, 2011), and decide the allocation ofconstruction/improvement grants among airports (Ghobrial, 1997),thesizeof airtrafccontrollerworkforce, andtheprovisionofnavigation and communication services. GA demand forecastserves as a fundamental component in evaluating the newconceptsand technologies suggested to the National Aeronautics and SpaceAdministration(NASA)tomeetthenational requirementforanimproved air trafc management system (Wingrove et al., 2002).ForecastsofGAdemandarealsoutilizedbymanylocal govern-mentsandairportplannerstosupportoperationalplanningandpersonnel requirements, make investment decisions related to thedevelopment of anairport andthecommunityaroundit, andevaluate the need for additional aviation facilities.1.1. Objective of this studyTheobjectiveof thispaperistodevelopanairport-level GAdemandforecast model. Inthisstudy, airport-level GAdemandrefers to the number of GA operations (including both takeoffs andlandings) at an airport. GA operations are classied into two types:local operations and itinerant operations. Local operations refer toaircraft operating in the trafc pattern or within sight of the tower,or aircraftknownto bedepartingor arrivingfromightinlocalpractice areas, or aircraft executing practice instrument approachesattheairport. Itinerantoperationsrefertooperationsofaircraftgoing from one airport to another. The FAA reports all aircraft op-erations other than local operations as itinerant.1.2. Literature review on GA demand modelsGA demand models can be classied into two categories. Modelsin therst category focus on studying the macro-level relationshipbetween GA demand and demand determinants (e.g., social-economic factors). Ratchford (1974) developed a theoreticalmodel formeasuringthequantityandpriceof theserviceowobtained from a durable good. It is concluded that the GA servicequantityissensitivetoincomeandGApricechanges. Vahovich*Corresponding author. Tel.: 1 5402316635; fax: 1 5402317532.E-mail address: TAO81@VT.EDU (T. Li).Contents lists available at ScienceDirectJournal of Air Transport Managementj ournal homepage: www. el sevi er. com/ l ocat e/ j ai rt ramanhttp://dx.doi.org/10.1016/j.jairtraman.2014.07.0030969-6997/ 2014 Elsevier Ltd. All rights reserved.Journal of Air Transport Management 40 (2014) 192e206(1978)alsofound thatincome andaircraft operatingcosthaveasignicant impact onthenumberof hours ownbyindividualowners (vs. companies). Archibald and Reece (1977) used a hedonicpriceequation(i.e., a semi-logarithmic function) tomodel therelationship between aircraft characteristics and price index. Theirresults support the hypothesis that the demand for fuel efciencyincreases if there is an energy crisis (e.g., oil embargo). Li and Trani(2013)showedthattherelativefuelprice-fuelpricecomparedwith disposable income per capita eis a signicant determinant oftheutilizationofpistonengineaircraft. Inaddition, theirresultssuggest that the elasticity of the utilization rate with respect to therelative fuel price is about 0.6.To effectivelyallocate limitedresources among the diversenetworkofairportsintheU.S. requiresGAdemandforecastsatcountyor airport level. However, models inthe rst categoryusuallylackthecapabilitytoprovidecountyorairport-levelde-mandforecasts. Fromthispoint of view, modelsinthesecondcategory are more important because they are developed to modelthemicroscopicrelationshipbetweencountyorairport-levelGAdemand and local demand determinants. Baxter and Philip Howrey(1968) investigatedthedeterminantsof county-level GAopera-tions. Theirstudyshowsthatpopulation, personal income, agri-culture employment, number of airports and airport quality (e.g.,the fraction of airports with runway lights and paved runway) couldbesignicantdeterminants. Wingroveetal. (2002)developedatop-down approach to forecast GA eet and itinerant GA operationsat an airport. The signicant demand determinants they identiedare similar to those reported by Baxter and Philip Howrey (1968).Ghobrial and Ramdass (1993), Wingrove et al., (2002) developed aneconometricmodel toforecastdemandatGAairports. Ghobrial(1997) further extendedthe model byenhancing the model'sstructure and using a larger sample of GA airports for model cali-bration. In particular, theirresultsshow thatsupplyfactors(e.g.,controltowerandrunway)aresignicantdeterminantsof ightoperations. Hoekstra (2000) used a linear model to forecast the GAoperationatsmall toweredandnon-toweredairports. Hefoundthat the number of based aircraft at an airport, income per capita,and whether the airport is certicated for commercial services areamong the important demand determinants. GRA (2001b) renedHoekstra's model by considering more local variables (e.g. presenceof a ight school at the airport) and more data in model calibration.Dou et al. (2001) also developed a linear model to forecast GA de-mandbyusinglocal social-economicanddemographic factors.However, their model has a low goodness oft (i.e., low R-square).Baik et al. (2006), Dou et al. (2001) utilized gravity models to studythe distribution of GAdemand among airports. Both of their modelsshowthat the distance between airports is a particularly importantfactor for the distribution of GA demand.The model presented in this paper can be considered anextension of the econometric model developed by Ghobrial (1997).Ourstudydepartsfromthepreviousoneinthefollowingthreeaspects:1)The focus of the study in Ghobrial (1997) is the aviation demand(i.e., the combination of commercial aviation and GA demand) atGA airports, while the focus of our study is the GA demand atanyairport. InGhobrial(1997), itinerantoperationsandlocaloperationsarecombinedandmodeledtogether (i.e., byonemodel). In our study, itinerant GA operations and local GA op-erations are modeled separately (by two different models). Thismeans our study provides a more detailed GA demand forecast.2)The impact of fuel price is considered in the modeling. It is wellknown that the GA demand in the U.S. has been declining overthe past decade. This decline in GA activities cannot becompletely explained by the country's economic conditions. TheFAAbelievesthatthedeclineispartiallyduetothehighfuelprice (FAA, 2010a). The historical trend of GA fuel prices supportsFAA's belief. From 2000 to 2011, the price 1of Jet-A fuel increasedby about 120%. In 2000, the cost of purchasing 1000 gallons ofJet-A fuel accounted for about 8.5% of the personal income percapita; however, this number increased to about 13.7% in 2011.The survey conducted by (Kamala et al., 2012) also supports thisbelief. In the survey, more than half of the responses indicatedthatcostswereasignicantreasonforwhytheydidnot y.However, to the authors' best knowledge, the effect of fuel priceis not consideredintheexistingairport-level demandfore-casting models. From this point of view, it is questionable thatthose models could adequately capture the impact of fuel pricein their forecasts. In commercial aviation and many other modesof transportation, fuel price has been studied as an independentfactor, and its impact on travel demand and travel cost has beenproved to be signicant. For example, in the area of mass transit/transportation, thestudyinMaghelal (2011) showsthat fuelprices could signicantly impact the U.S. transit ridership. Thestatistical model suggests that an additional one dollar increaseto the average fuel cost could result in an increase in transit tripsby484%. Macharis et al. (2010) usedaGIS-basedmodel toinvestigate the impact of fuel price on the competition betweenintermodal transport (e.g., rail and barge) and unimodal trans-port (e.g., road). They found that demand shifts to intermodaltransport if the fuel price increases. In the area of personal ve-hicles, Gallo (2011) found that a 22% increase in fuel price cor-respondstoa2.56%decreaseindemandforca

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