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Refinements to a procedure for estimating airfield capacity
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- Author(s) / Creator(s)
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This paper presents a method for obtaining airfield capacity estimates, using historical data from the Federal Aviation Administration’s Aviation Systems Performance Metrics (ASPM) database. The process first involves merging individual flights and quarter-hour airport runway operations datasets from ASPM to create a new dataset. Data for Newark International Airport and San Diego International Airport from 2006 to 2011 was used. Secondly, filters for meteorological condition, runway configuration, called rates, and fleet mix were applied to the two airport datasets. The filtered datasets were then used in a censored regression model of capacity that includes queue length (number of aircraft waiting to arrive/depart) and arrival/departure throughput count splits as independent variables. These attributes were found to impact airfield capacity at statistically significant levels, and parameters had expected signs and magnitudes. Additionally, capacities under ideal conditions were found to be reasonably close to other sources. The model also confirmed that average capacities at EWR during hours when a Ground Delay Program (GDP) was running were lower than when there was no GDP in effect. The method described in this paper can be used to more precisely quantify airfield capacities in specific conditions of particular interest to air traffic controllers and airport operators, to better facilitate decisions that rely heavily on a good understanding of capacity in these conditions. The data exploration and preparation undertaken as part of the study reveals some of the finer points of the ASPM data and how it can be used in a more meaningful way for airfield capacity estimation.
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- Date created
- 2019-11-12
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- Type of Item
- Article (Draft / Submitted)