how are airport context and service related to general aviation aircraft operations? transportation...
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How Are Airport Context and Service How Are Airport Context and Service Related to General Aviation Aircraft Related to General Aviation Aircraft
Operations?Operations?
Transportation Research Board ConferenceTransportation Research Board Conference
January 16, 2002January 16, 2002
Peter A. JolicoeurPeter A. JolicoeurRicondo & Associates, Inc.Ricondo & Associates, Inc.
San Francisco, CaliforniaSan Francisco, California
Asad J. KhattakAsad J. KhattakCarolina Transportation Carolina Transportation ProgramProgram
University of North CarolinaUniversity of North Carolina
Chapel Hill, North CarolinaChapel Hill, North Carolina
Carolina Transportation Program
General aviation
Everything but commercial airlines and the military
GA benefits: Accessibility, economy
Growth sector: Improving technology
Previous research
Research goal
Identify airport service and contextual variables associated with GA operations
ContextService
Why? Planning implicationsAnticipate future infrastructure needsChoose between improvement alternativesAttract general aviation aircraft away from primary, congested airports
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
Data
SourcesFAA, NCDOT, U.S. Census, U.S. Dept. of Commerce, NC Dept. of Commerce, NC Office of State Planning, AOPA.
GIS manipulationLongitudinal and cross-sectional analysis
41 airports12 years of data (1988-1999)471 observations
Dependent variable
Terminal Area Forecast (ATCT)
Master Record Survey (FAA 5010)
NCDOT Noise Counter Survey
Tower controlled airport?
Use TAF data Noise counter data?
Adjust 5010 data Use unadjusted 5010 data
YES
NO
NO
YES
Airports in study
Analysis
Estimate OLS, between, fixed-effects, and random-effects regressions
Use non-transformed and logarithmically transformed data
Identify significant independent variables
Hypothesized Factors
Supply (service)
Demand (population)
Land use (surrounding development)
Location (proximity to highway)
Transportation (ops. at primary airport)
Regression models
Basic time-series / cross-sectional model:“i” airports over “t” time periods
iiii xy
itiitit xy
Between regression:OLS estimated with averages for each “i” airport
Regression models
Fixed-effects (within) regression:No generalized constant; unit-specific residual calculated for each airportModel can not estimate β for regressors that do not vary over time (highway distance)
)()()y( i iitiitit xxy
Regression modelsRandom-effects regression:
Weighted average of results estimated with between- and fixed-effects regressionΘ is a function of variance of and If the unit specific residual is zero, Θ is zero allowing simple OLS regressionIf variance of the error term is zero, Θ is one giving equation same form as fixed-effects regression
)()1()xx()1()yy( iitiiitiit
Coefficients:Contextual Variables
GA_OPS Independent Variable β Z-stat
POP_5M: Population within five miles of the airport. -0.036 -0.48
NEARPOP: Population of the town or city served by the airport, in thousands. -2.320 -0.16
HOTEL_1: Hotel within 1 mile of the airport. 21479 5.69 HWY_L3: Highway within 3 miles of airport 7941 2.04 HWY_G20: Highway more than 20 miles from airport 2242 0.47
OPSRWYLN: Nat'l log of annual ops. per runway at nearest primary airport during the previous year.
3562 2.03
GSP: Gross state product, in billions of year 2000 dollars. -33.0 -1.71
REG_PCIN: Regional per-capita income, in year 2000 dollars (x 1000). 75.6 0.16
Coefficients:Service Variables
GA_OPS Independent Variable β Z-stat
RWY_G4K: Runway length greater than 4000 feet. 2024 1.08
PREC_APP: Availability of a precision instrument approach. 1848 1.30
NONP_APP: Availability of a non-precision instrument approach. 8768 3.93
AVIONICS: Avionics repair service available. 7.74 0.01 CHARTER: Charter aircraft service available. 3458 2.40
INSTRUCT: Pilot instruction available. 4969 2.15 FUEL: Fuel sales available. -3093 -1.41 REPAIR: Aircraft repair service available. 3878 3.54
Constant -28968 -1.44 Overall R2 0.721
Results: Airport Context
Hotel: 21,500 more operationsProxy 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
Results: Airport Context
Ground access: 7,900 more operationsAir 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
Results: Airport Context
Population and Employment: Not significantRefine with GIS: Travel time to airport
“Catchment area” based on level of service
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
Study limitations
Dependent variableDifficult to obtainNorth Carolina noise counter surveys
Model specificationMore or better defined variables (population, firm location and employment)“Quality” of operations
Model structure: association, not causalityTwo-stage least squares
Contribution
Unique dataset created with GIS
Presentation of data from spatial perspective
Use of rigorous statistical analysis
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