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How Are Airport Context and Service Related to General Aviation Aircraft Operations?

Carolina Transportation Program. Peter A. Jolicoeur Ricondo & Associates, Inc. San Francisco, California. How Are Airport Context and Service Related to General Aviation Aircraft Operations?. Asad J. Khattak Carolina Transportation Program University of North Carolina

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How Are Airport Context and Service Related to General Aviation Aircraft Operations?

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

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

  3. 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

  4. Impacts Conceptual structure General Aviation aircraft operations Airport service Airport context • 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

  5. 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 (1988-1999) • 471 observations

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

  7. Airports in study

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

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

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

  11. 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)

  12. 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

  13. Coefficients:Contextual Variables

  14. Coefficients:Service Variables

  15. 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

  16. 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

  17. Results: Airport Context • Population and Employment: Not significant • Refine with GIS: Travel time to airport • “Catchment area” based on level of service

  18. 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

  19. 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

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

  21. 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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