Highlights An alternative way of acquiring input parameters for trajectory calculations is proposed. Machine learning tries to predict performances based on recorded similar flights from the past. User are often left with default parameters, which are not always the best fit. A cheap alternative until planned downlinked trajectory will be in place.

    Abstract Accurate prediction of aircraft position is becoming more and more important for the future of air traffic. Currently, the lack of information about flights prevents us to fulfill future demands for the needed accuracy in 4D trajectory prediction. Until we get the necessary information from aircraft and until new more accurate methods are implemented and used, we propose an alternative method for predicting aircraft performances using machine learning from historical data about past flights collected in a multidimensional database. In that way, we can improve existing applications by providing them better inputs for their trajectory calculations. Our method uses flight plan data to predict performance values, which are suited individually for each flight. The results show that based on recorded past aircraft performances and related flight data we can effectively predict performances for future flights based on how similar flights behaved in the past.


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    Title :

    Prediction of aircraft performances based on data collected by air traffic control centers


    Contributors:


    Publication date :

    2016-10-27


    Size :

    16 pages




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English





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