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.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Machine learning model for aircraft performances


    Contributors:


    Publication date :

    2014-10-01


    Size :

    549087 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Machine learning model for aircraft performances

    Hrastovec, Marko / Solina, Franc | IEEE | 2014

    Free access

    Comparison of aircraft performances

    Mettam, H.A. | Engineering Index Backfile | 1927


    Methods of measuring aircraft performances

    Tizard, H.T. | Engineering Index Backfile | 1917



    An Aircraft Deployment Prediction Model Using Machine Learning Techniques

    Ukai, Takaya / Chao, Hsun / DeLaurentis, Daniel A. | AIAA | 2017