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How Are Airport Context and Service Related to General Aviation Aircraft Operations? Transportation Research Board Conference January 16, 2002 Peter A.

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Presentation on theme: "How Are Airport Context and Service Related to General Aviation Aircraft Operations? Transportation Research Board Conference January 16, 2002 Peter A."— Presentation transcript:

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2 How Are Airport Context and Service Related to General Aviation Aircraft Operations? Transportation Research Board Conference January 16, 2002 Peter A. Jolicoeur Ricondo & Associates, Inc. San Francisco, California Asad J. Khattak Carolina Transportation Program University of North Carolina Chapel Hill, North Carolina Carolina Transportation Program

3 General aviation Everything but commercial airlines and the military GA benefits: Accessibility, economy Growth sector: Improving technology Previous research

4 Research goal Identify airport service and contextual variables associated with GA operations Context Service Why? Planning implications Anticipate future infrastructure needs Choose between improvement alternatives Attract general aviation aircraft away from primary, congested airports

5 Conceptual structure General Aviation aircraft operations Airport context Impacts Airport service Primary runway length Instrument approach Avionics repair Charter service Rental aircraft Pilot training Fuel sales Repair facilities DEMAND Pop. & Employ. Income & Productivity LAND USE Surrounding develop. SPATIAL FACTORS Proximity to city & highway TRANSPORTATION Volume of traffic at primary airport Accessibility Economy Noise Delay Capacity

6 Data Sources FAA, NCDOT, U.S. Census, U.S. Dept. of Commerce, NC Dept. of Commerce, NC Office of State Planning, AOPA. GIS manipulation Longitudinal and cross-sectional analysis 41 airports 12 years of data ( ) 471 observations

7 Dependent variable Terminal Area Forecast (ATCT) Master Record Survey (FAA 5010) NCDOT Noise Counter Survey Tower controlled airport? Use TAF dataNoise counter data? Adjust 5010 data Use unadjusted 5010 data YES NO YES

8 Airports in study

9 Analysis Estimate OLS, between, fixed-effects, and random-effects regressions Use non-transformed and logarithmically transformed data Identify significant independent variables

10 Hypothesized Factors Supply (service) Demand (population) Land use (surrounding development) Location (proximity to highway) Transportation (ops. at primary airport)

11 Regression models Basic time-series / cross-sectional model: i airports over t time periods Between regression: OLS estimated with averages for each i airport

12 Regression models Fixed-effects (within) regression: No generalized constant; unit-specific residual calculated for each airport Model can not estimate β for regressors that do not vary over time (highway distance)

13 Regression models Random-effects regression: Weighted average of results estimated with between- and fixed-effects regression Θ is a function of variance of and If the unit specific residual is zero, Θ is zero allowing simple OLS regression If variance of the error term is zero, Θ is one giving equation same form as fixed-effects regression

14 Coefficients: Contextual Variables

15 Coefficients: Service Variables

16 Results: Airport Context Hotel: 21,500 more operations Proxy for commercial development Association, but not causation Granger test: Determine causality based on what information lag in one variable (hotel) provides on other variable (operations) Improvement: Direct data on surrounding land use

17 Results: Airport Context Ground access: 7,900 more operations Air trips expected to be multimodal Operations per runway at primary airport: 1% increase = 3,600 more operations Captures regional demand Improvement: Delay at Primary Airport

18 Results: Airport Context Population and Employment: Not significant Refine with GIS: Travel time to airport Catchment area based on level of service

19 Results: Airport Service Non-precision approach: 8,800 more operations Aircraft charter service: 3,500 more operations Pilot instruction: 5,000 more operations Repair service: 3,900 more operations Not significant: Runway length, precision approach, fuel, avionics repair

20 Study limitations Dependent variable Difficult to obtain North Carolina noise counter surveys Model specification More or better defined variables (population, firm location and employment) Quality of operations Model structure: association, not causality Two-stage least squares

21 Contribution Unique dataset created with GIS Presentation of data from spatial perspective Use of rigorous statistical analysis

22 Implications Planning: Local & regional Ground access GPS approaches: Increase system capacity & airport operations Aviation services Air travel demand will likely increase with improved technology: Anticipate future system needs


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