Mistakes on a Plane: Extrapolating Info and Determining Abnormalities in Flight Data

MARC-GREGORY R. DIXON, BROOKE L. BROWN, BRENDAN ARMSTRONG, BRENNAN S. RINGEL, ERIC N. ZIELONKA, and ALEKSANDR W. FRITZ

The ability to monitor air traffic effectively is of the upmost importance to our nation's security. For this reason, companies such as ASRC Federal Missions Solutions have made it their priority to push new techniques of interpreting flight data to the forefront using innovative tools and analytics. Our project focused on using real flight data and machine learning to discover anomalies and to classify flights based on their origins and destinations. Our research involved studying various machine learning algorithms and finding which ones worked most successfully with our data. In addition, we have also worked on implementing a multi-dimensional visualization layer to allow the user to interface with the flight data more directly, further increasing the accessibility of the project.