Determination of a probabilistic model forflight path prediction

Abstract: Flightradar24.com is a website providing a flight tracking service that has a coverage spanning, a major part of the world. In some geographical areas though, the website is unable to get information from the airplanes. One such area is a large part of the Atlantic Ocean. When the website loses track of a plane, it keeps plotting it for about ten minutes keeping the latest givens peed and heading, before letting it disappear from view. In this degree project, an attempt is made to improve the model used by the website, using statistical methods and theories of machine learning. Data for a large amount of flights is observed. The data used includes the speed of the airplanes, their positions, their altitude and their headings, as well as the time when the flight took place. There is also some basic information about the flight, such as airplane type and flight number. By finding connections and relations in this data, a probabilistic model is created. The model is then used to predict where a flight outside the coverage of Flightradar24.com is at any given time. Specifically, the model is used to predict the flight path of airplanes over the Atlantic Ocean. Finally, the model is tested and found to give a more accurate prediction than the existing model for a number of flights.