Refinements to a Procedure for Estimating Airfield Capacity

This paper presents a method for obtaining airfield capacity estimates using historical data from FAA's Aviation System Performance Metrics (ASPM) database. The process first involves merging individual flights and quarter-hour airport runway operations data sets from ASPM to create a new data set. Data for Newark Liberty International Airport (EWR) in New Jersey and San Diego International Airport in California from 2006 to 2011 were used. Then, filters for meteorological condition, runway configuration, called rates, and fleet mix were applied to the two airport data sets. The filtered data sets were then used in a censored regression model of capacity that included queue length (number of aircraft waiting to arrive or depart) and arrival–departure throughput count splits as independent variables. These attributes were found to affect 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 could 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 reveal some of the finer points of the ASPM data and how they can be used in a more meaningful way for airfield capacity estimation.