Machine learning model for aircraft performances

Marko Hrastovec and Franc Solina (2014) Machine learning model for aircraft performances. In: 33rd Digital Avionics System Conference, 5-9 October 2014, Colorado Springs, CO.

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Abstract

This paper presents new idea how trajectory calculations could be improved in order to match real flights better. Exact trajectory calculation is important for future of air traffic control, because it is one of the enablers for safe traffic increase. Methods used to calculate trajectories are based on aircraft types and their performances mainly. However, we believe that there are many other influencing factors which should be taken into account. We collect available data about flights and store them into a multi-dimensional database. Knowledge accumulated in this database is the basis for aircraft performances prediction using machine learning methods. In that way the prediction is not based on aircraft type alone, but also on other attributes like aerodrome of departure, destination and operator. There attributes indirectly imply to procedures, operator’s best practices, local airspace characteristics, etc. and enable us to make better predictions of aircraft performances. Predictions in this case are not static but tailored to every particular flight.

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