Detecting air traffic controller interventions in recorded air transportation system data

Yul Kwon, Purdue University


In this study, I propose a systematic method of detecting aircraft deviation due to air traffic controller (ATC) intervention. The aircraft deviations associated with ATC interventions are detected using a heuristic algorithm developed from analyzing the actual positions of an aircraft to its filed flight plan when the aircraft trajectories were identified as having an encounter in a loss-of-separation incident. An actual (closed-loop) flight trajectory of the Cleveland Air Route Traffic Control Center (ZOB ARTCC) was collected from the FlightAware database. This was compared with the corresponding planned (open-loop) trajectory dataset generated by the Microsoft© Flight Simulator X (FSX). I implemented a conflict-detection algorithm in Matlab to identify open-loop flight trajectories that encounters in loss-of-separation. I analyzed the differences between the closed-loop and open-loop flight trajectories of aircrafts that were identified to have encounters in loss of separation. The analysis identified operationally significant deviations in the closed-loop trajectory data with respect to the horizontal paths of the aircrafts. I then developed and validated a heuristic algorithm, the ATC intervention detection algorithm, based on the findings from the analysis. When used with a test dataset to validate the algorithm, it achieved an 85.7% detection rate in detecting horizontal deviations made by the ATC in resolving identified conflicts, and a false-alarm rate of 68%. In addition to the ATC intervention detection algorithm, I present in this paper an analysis of deviated flight trajectories in an effort to display how the presented methodology can be utilized to provide insight into air traffic controller resolution strategies.




Landry, Purdue University.

Subject Area

Aerospace engineering|Industrial engineering|Transportation planning

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