Trajectory Clustering and an Application to Airspace Monitoring

Shared by Vlad Popescu, updated on Feb 07, 2011


Author(s) :
Maxime Gariel, Ashok Srivastava, Eric Feron

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 (ATC) to guide aircraft, ensure the safety of the
airspace, and to maximize the runway occupancy. Even though
standard procedures are used by ATC, the control of the aircraft
remains with the pilots, leading to a large variability in the flight
patterns observed. Two methods to identify 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 the 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 a
safe separation between aircraft.

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