A Categorical Model for Airport Capacity Estimation Using Hierarchical Clustering

Abstract

Motivated by the need for very inexpensive, easily updated, first-order-accurate estimates of airport capacity required in system-wide analyses, we propose a novel approach to generate a predictive categorical model. The underlying hypothesis tested in this work is that for the same weather conditions airports with a similar runway configuration and fleet mix will have similar capacities. Accordingly, if airport categories with known capacity are defined a-priori on the basis of similarity in fleet mix and runway configuration, then a membership function to the set of categories essentially constitutes a predictive model. We test this hypothesis by formulating and implementing such a model in order to examine its feasibility and discuss key practical considerations. Verification demonstrates model fit error within 4% with a categorical training set of 35 major United States airports. Validation against European airports for model representation error is limited by data availability but shown to be in the order of 7%. Results suggest that elemental runway configurations are the primary driver for categorical definition, and variations within each category can be associated to fleet mix variations. The implementation of the proposed method to generate other such models with different data sets is encouraged.

Publication
Journal of Aerospace Operations, vol. 4, no. 4, pp. 245-273