Flight Trajectory Clustering: a framework that uses Planned Route data

Abstract

Clustering is an efficient method for handling large amounts of complex data. More specifically, k-means clustering is an optimized algorithm based on Euclidean distance measurement that has been applied to aircraft trajectory classification. In this paper we present a research line based on performing a preprocessing of trajectory coordinates and flight plan data to obtain additional variables, as adapted to the k-means clustering algorithm as possible, in order to support supervised trajectory classification.