Trajectory clustering and an application to airspace monitoring

Maxime Gariel, Ashok N. Srivastava, Eric Feron

Research output: Contribution to journalArticlepeer-review

156 Scopus citations

Abstract

This paper presents a framework aimed at monitoring the behavior of aircraft in a given airspace. Trajectories that constitute typical operations are determined and learned using data-driven methods. Standard procedures are used by air traffic controllers (ATCs) to guide aircraft, ensure the safety of the airspace, and maximize runway occupancy. Even though standard procedures are used by ATCs, control of the aircraft remains with the pilots, leading to large variability in the flight patterns observed. Two methods for identifying typical operations and their variability from recorded radar tracks are presented. This knowledge base is then used to monitor the conformance of current operations against operations previously identified as typical. A tool called AirTrajectoryMiner is presented, aiming at monitoring the instantaneous health of the airspace, in real time. The airspace is healthy when all aircraft are flying according to typical operations. A measure of complexity is introduced, measuring the conformance of current flight to typical flight patterns. When an aircraft does not conform, the complexity increases as more attention from ATC is required to ensure safe separation between aircraft. © 2011 IEEE.
Original languageEnglish (US)
Pages (from-to)1511-1524
Number of pages14
JournalIEEE Transactions on Intelligent Transportation Systems
Volume12
Issue number4
DOIs
StatePublished - Dec 1 2011
Externally publishedYes

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