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A Method for Identifying General Aviation Airports That Are Candidates for Runway Extensions: a Planning Model for State Aviation Systems

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A Method for Identifying General Aviation Airports That Are Candidates for Runway Extensions: a Planning Model for State Aviation Systems Journal of Aviation/Aerospace Education & Research Volume 2 Number 2 JAAER Winter 1992 Article 6 Winter 1992 A Method for Identifying General Aviation Airports that are Candidates for Runway Extensions: A Planning Model for State Aviation Systems Randall G. Holcombe Henry B. Burdg Follow this and additional works at: https://commons.erau.edu/jaaer Scholarly Commons Citation Holcombe, R. G., & Burdg, H. B. (1992). A Method for Identifying General Aviation Airports that are Candidates for Runway Extensions: A Planning Model for State Aviation Systems. Journal of Aviation/ Aerospace Education & Research, 2(2). https://doi.org/10.15394/jaaer.1992.1068 This Article is brought to you for free and open access by the Journals at Scholarly Commons. It has been accepted for inclusion in Journal of Aviation/Aerospace Education & Research by an authorized administrator of Scholarly Commons. For more information, please contact [email protected]. Holcombe and Burdg: A Method for Identifying General Aviation Airports that are Candi A METHOD FOR IDENTIFYING GENERAL AVIATION AIRPORTS THAT ARE CANDIDATES FOR RUNWAY EXTENSIONS: A PLANNING MODEL FOR STATE AVIATION SYSTEMS Randall G. Holcombe and Henry B. Burdg ABSTRACT One of the most important characteristics of an airport is the length of its longest runway: that length determines the types ofaircraft that can use the airport andprovides a margin ofsafety for users. Arunway extension, therefore, would enhance the utility ofmany airports. But resources are scarce, and iffunds are to be allocated at the state level, state officials need a method for determining which airports could best use a longer runway. Consideration of demographic factors enhances the traditional engineering analysis approach to runway evaluation. This combined approach is more consistent with the goals of comprehensive airport planning. This paper describes a model that uses regression analysis to compare airports in Alabama, taking into account a number of different demographic factors and airport related factors. A linearregression model was used to evaluate howan airport's runway length compared to others around the state with similar characteristics. The residuals from the regressions were used to identify those airports with relatively short runways, considering their other characteristics. The regression analysis identified 22 of the 106 public-use airports in Alabama as having runways substantially shorter than their other characteristics would predict. One of the most important 1988) estimated that $285 million categories: demographic factors characteristics of an airport is the (1988-1989) in state funds were (Federal Aviation Administration length of its longest runway, spent on state airport develop­ [FAA], 1972) and airport related because it will determine the ment projects. In many situations factors (Horonjeff & McKelvy, types of aircraft that can use the it appears that these funds are 1983). In this paper 10 demo­ airport and because longer run­ politically directed rather than graphic factors are considered. ways provide a margin of safety proactively planned. including the population of the for any aircraft using the airport Resources are scarce and if county where the airport is (Ashford & Wright. 1979). A short state airport development funds located. population in the runway limits the usefulness of are to be allocated effectively. immediate vicinity of the airport, an airport and restricts the ability state officials need a method for population growth in the area, to accommodate the current evaluating candidate airportsthat population density, and the corporate fleet. Many of the could best use longer runways. number of nearby businesses, general aviation airports were This paper describes a model employees, and payroll. The designed and constructed 35 or that uses regression analysis to paper also considers 23 airport more years ago. meeting the compare airports in Alabama. factors, including the number of aircraft requirements of that day. taking into account a number of based aircraft and operations at For the most part the smaller different factors. The work the airport, services offered. and community airport has not described here is included in a the locations of other airports. In developed as fast as the larger study of general aviation certain geographical settings. it population it is to serve. A airports in Alabama conducted is important to consider the runway extension, therefore. for the State of Alabama. Depart­ variation in airport elevations. would enhance the utility of ment of Aeronautics. March runway gradient, and design many airports. 1987. With the results from this reference temperatures. These There are some 5,598 general model. state officials can better factors do affect runway length. aviation airports and 568 general evaluate proposals for runway In Alabama the variation is repre­ aviation and commercial airports extensions. sented by a narrow range and in the United States. A recent Factors determining the thus excluded from the analysis. study (National Association of appropriate runway length can For exar.lple. the design refer­ State Aviation Officials [NASAO], be divided into two general ence temperatures range from Published by Scholarly14 Commons, 1992 JAAER, Winter 1992 1 Journal of Aviation/Aerospace Education & Research, Vol. 2, No. 2 [1992], Art. 6 Runway Extension Planning Model 89-94 degrees F. Another con­ operate from a runway 2,000 feet conjunction with the runway sideration can be the initial long, but a heavy single-engine engineering specifications design function of the airport. or multi-engine craft needs a required by the airport'S past For example, a few Alabama air­ runway of at least 3,000 feet. designated Critical Aircraft. ports were initially built as mili­ Although small jets can take off Ideally, a state would compare tary airfields and converted to and land on 4,000 foot runways, its airports, taking into account public general aviation facilities. larger jets require 5,000 feet or the differences in their environ­ These tend to have longer run­ more. Runways of 10,000 feet ments, and compile a list of ways than airports specifically are common at the major com­ those that could benefit most designed and constructed for mercial service airports (FAA, from runway extensions. Many general aviation use. Additional 1983). airports could benefit from longer variables can be added to the These runway lengths are runways, but this paper presents regression models as needed to approximations. Under many the results of a regression model accommodate a region's unique circumstances, the aircraft that identifies those with features. described above could operate relatively short runways, taking A linear regression model was from shorter runways, even other factors into account. The used to compare the lengths of though a margin of safety would remainder of this paper explains the runways at general aviation be lost. An experienced pilot, for howthe analysis was undertaken airports around the state. Several example, would have no trouble for Alabama's airports. regression equations were esti­ landing a typical civilian training METHOD· DATA USED mated with runway length as the aircraft in 2,000 feet of runway, IN THE STUDY dependent variable. One regres­ but the runway would probably Alabama has 106 public-use sion used all 32 independent be too short for a student pilot or airports, as identified by Federal variables; others used only a pilot who flies infrequently. The Aviation Administration (FAA) demographic factors, airport trade-off is clear. A longer run­ 1986 records. The FAA maintains data, or number of operations at way will make an airport safer a Form 5010-1, Airport Master the airport. The residuals from and more useful, but runway Record, for each federally the regressions were used to extensions are expensive. If registered airport in the United identify those airports with runway extension projects are States. This study analyzes all relatively short runways, eligible for state airport develop­ public-use airports in the state, consideringtheirothercharacter­ ment funds, state airport officials including those that are privately istics. The regression analysis need a means of identifying owned, for which the FAA main­ identified 22 of the 106 public­ those airports that would benefit tains a 5010-1 form. The study use airports in Alabama as most from longer runways. excludes military airports, having runways substantially Examining length alone is heliports, and seaplane bases. shorter than their other charac­ insufficient: a 3,500-foot runway Table 1 presents a list of the teristics would predict. adequate for an infrequently variables collected for each After the regression analysis is used airport catering to single­ airport and used in the analysis. explained, the airports are engine traffic, would be a signi­ The first 10 are demographic examined in detail to determine ficant constraint for a community variables that characterize the why their runways are shorter that has the potential for many population that would use the than expected and to explain operations involving larger airport. how the regression results can aircraft, including jets. Any The variable County is the be applied to make a recommen­ determination ofthe adequacy of population ofthe county in which dation to lengthen a runway. a runway must take into account the airport is located. Most RUNWAY LENGTH AND the economic and demographic counties in Alabama
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