Collision Risk Models (CRM) are used to assess the collision risk for proposed new airspace procedures. Examples of air-to-air collision scenarios in the terminal airspace include: (1) a pair of flights on non-conflicting straight tracks (e.g., parallel tracks) where one experiences a mechanical or navigational failure resulting in a blunder into the other path, and (2) a pair of flights on separated tracks requiring one or more lateral or vertical maneuvers (e.g., turn to final on parallel approach) and one aircraft fails to perform the maneuver resulting in a conflict with the other aircraft. For the purpose of simulating pairs of flight tracks in the terminal area with sufficient sample size to calculate a rare-event collision risk, it is helpful to focus simulations on the scenarios with the highest potential for collision. This paper describes the application of the Aviation Risk Identification and Assessment (ARIA) metric to identify collision risk scenarios for the purpose of CRM for terminal areas. An analysis of terminal area arrivals to Runway 34 at Zurich airport identified the highest collision risk (i.e., lowest ARIA scores) for combinations of lateral and vertical scenarios: (1) head-on opposite courses, (2) side-on with intersecting courses, (3) same course, (4) intersecting pair of descending trajectories, (5) descending towards another level flight (i.e., step-downs), and (6) pair in level flight. The most frequent combination of high-risk maneuvers was step-down descents with opposite courses (42 % ). All of these high-risk maneuvers are the result of maneuvers introduced by air traffic control for merging and spacing. These results highlight the importance of modeling off-nominal flight tracks for the purpose of CRM.


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    Title :

    Identification Of Arrival Airspace Maneuvers With High Collision Risk


    Contributors:


    Publication date :

    2024-09-29


    Size :

    2355183 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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