Airspace partitioning using flight clustering and computational geometry

Abstract

We propose and analyze the use of a clustering algorithm to group flight positions together as a component of algorithmic airspace partitioning. The clustering problem is formulated as a constrained clustering problem, and we present novel heuristics for this problem. A primary hypothesis of this work is that the clustering algorithm approach for airspace partitioning allows dynamic density (DD) metrics to be implicitly manipulated in the airspace partitioning process. The analysis results demonstrate the efficacy of the constrained clustering algorithm heuristics and the successful control of DD results in the generated airspace partition.