System and method for predicting aircraft runway capacity

    公开(公告)号:US10783288B1

    公开(公告)日:2020-09-22

    申请号:US15671170

    申请日:2017-08-08

    Abstract: A runway capacity forecast system includes machine instructions stored in a non-transitory computer readable storage medium, the machine instructions, when executed, causing a processor to access data items related to a runway of interest for a time horizon of interest, the data items comprising environment factors for the runway of interest and the time horizon of interest, flight operation factors, and aircraft performance factors for aircraft scheduled on the runway of interest and during the time horizon of interest; extract data elements from the data items; reformat the data elements as analyzable data elements and store the analyzable data elements in an analyzable data structure; apply a probabilistic model to selected ones of the analyzable data elements to provide a forecast runway capacity for the runway of interest during the time horizon of interest the first product; and using the forecast runway capacity, determine one or more impacts based on the forecast capacity.

    Optimizing Aircraft Flows at Airports Using Data Driven Predicted Capabilities

    公开(公告)号:US20210133370A1

    公开(公告)日:2021-05-06

    申请号:US17012332

    申请日:2020-09-04

    Abstract: A method for safe and efficient use of airport runway capacity includes receiving, at an air traffic control system at an airport, airport data related to movement areas of the airport, time data related to a time period, aircraft data related to a plurality of aircraft expected to operate into and out of the airport during the time period, and environmental data related to environmental conditions predicted for the airport during the time period. The method further includes computing a probability distribution for inter-aircraft spacing by applying the airport data, the time data, the aircraft data, and the environmental data to a trained Bayesian network, producing the probability distribution for the inter-aircraft spacing as an output observation of the trained Bayesian network, and, using the probability distribution and a confidence value, identifying an inter-aircraft spacing value for the plurality of aircraft expected to operate into and out of the airport during the time period.

    Optimizing aircraft flows at airports using data driven predicted capabilities

    公开(公告)号:US12026440B1

    公开(公告)日:2024-07-02

    申请号:US17985850

    申请日:2022-11-12

    CPC classification number: G06F30/20 G06F17/18 G06F2111/10

    Abstract: A method for use of airport runway capacity includes receiving, at an air traffic control system at an airport, airport data related to movement areas of the airport, time data related to a time period, aircraft data related to a plurality of aircraft expected to operate into and out of the airport during the time period, and environmental data related to environmental conditions predicted for the airport during the time period. The method further includes computing a probability distribution for inter-aircraft spacing by applying the airport data, the time data, the aircraft data, and the environmental data to a trained Bayesian network, producing the probability distribution for the inter-aircraft spacing as an output observation of the trained Bayesian network, and, using the probability distribution and a confidence value, identifying an inter-aircraft spacing value for the plurality of aircraft expected to operate into and out of the airport during the time period.

    Optimizing aircraft flows at airports using data driven predicted capabilities

    公开(公告)号:US11501039B2

    公开(公告)日:2022-11-15

    申请号:US17012332

    申请日:2020-09-04

    Abstract: A method for safe and efficient use of airport runway capacity includes receiving, at an air traffic control system at an airport, airport data related to movement areas of the airport, time data related to a time period, aircraft data related to a plurality of aircraft expected to operate into and out of the airport during the time period, and environmental data related to environmental conditions predicted for the airport during the time period. The method further includes computing a probability distribution for inter-aircraft spacing by applying the airport data, the time data, the aircraft data, and the environmental data to a trained Bayesian network, producing the probability distribution for the inter-aircraft spacing as an output observation of the trained Bayesian network, and, using the probability distribution and a confidence value, identifying an inter-aircraft spacing value for the plurality of aircraft expected to operate into and out of the airport during the time period.

Patent Agency Ranking