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