The newly developed Trajectory Option Set (TOS), a preference-weighted set of alternative routes submitted by flight operators, is a capability in the U.S. traffic flow management system that enables automated trajectory negotiation between flight operators and Air Navigation Service Providers. The objective of this paper is to describe and demonstrate an approach for automatically generating pre-departure and airborne TOSs that have a high probability of operational acceptance. The approach uses hierarchical clustering of historical route data to identify route candidates. The probability of operational acceptance is then estimated using predictors trained on historical flight plan amendment data using supervised machine learning algorithms, allowing the routes with highest probability of operational acceptance to be selected for the TOS. Features used describe historical route usage, difference in flight time and downstream demand to capacity imbalance. A random forest was found to be the best performing algorithm for learning operational acceptability, with a model accuracy of 0.96. The approach is demonstrated for an historical pre-departure flight from Dallas/Fort Worth International Airport to Newark Liberty International Airport.


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    Title :

    Using Machine-Learning to Dynamically Generate Operationally Acceptable Strategic Reroute Options


    Contributors:
    Evans, Antony (author) / Lee, Paul (author)

    Conference:

    Air Traffic Management Research and Development (ATM R&D) Seminar ; 2019 ; Vienna, Austria


    Publication date :

    2019-06-17


    Type of media :

    Miscellaneous


    Type of material :

    No indication


    Language :

    English