Air route traffic control centers (ARTCCs) operated by the Federal Aviation Administration (FAA) are responsible for efficiently and safely managing United States en route air traffic at altitudes of 18,000 feet and above. To achieve the FAA's mission, the ARTCC's human air traffic controllers monitor air traffic and ensure aircraft safety within partitioned airspace, called sectors, located in each ARTCC's boundaries. In order to achieve this, air traffic controllers must resolve potential conflicts that are identified through methods including manual inspection through looking at their display, or air traffic automated systems alerts. A standard en route conflict occurs if two aircraft travel within five nautical miles in the horizontal plane, while simultaneously flying within 1000 feet in the vertical plane. The controllers request pilots make changes to their intended trajectories to prevent a risk of violating separation distances. The FAA collects and archives the air traffic automation data corresponding to the predicted conflict events and information about the aircraft during their flight, such as: ground speed, vertical phase, horizontal phase, minimum separation distances and time postings. This paper describes algorithms for cataloging (detect and characterize) aircraft conflict resolutions, utilizing the data that is archived by air traffic automated systems. The automated systems only alert the air traffic controllers of potential conflicts. It is the human air traffic controllers that either perform a conflict resolution or determine if the alert is false. Our algorithms identify the maneuvers cleared by the air traffic controllers that occurred. We verify and validate our algorithms using real air traffic data. Characteristics of the traffic scenarios we used are not factors impacting our algorithms.


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    Title :

    Aircraft conflict resolution cataloguer


    Contributors:
    Yao, Chu (author) / Rusu, Adrian (author) / Danick, Andrew (author) / Hingorani, Ravina (author) / Toner, Ryan (author)


    Publication date :

    2017-09-01


    Size :

    1097869 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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