The management of air traffic is a complex task that requires ensuring the safety and efficiency of aircraft trajectories when transiting from one airspace sector into another. This work explores the use of historical flight data to predict if a flight will commit to the planned entry point when entering an airspace sector. To achieve this, we propose a feature engineering method that can be employed to convert raw flight data into a matrix which captures flight count information in predefined grids. This matrix is referred to as the Air Space Occupancy Grid (ASOG) and it captures the state of traffic in an airspace sector and its immediate vicinity. Experiments are performed using the Swedish Civil Air Traffic Control (SCAT) dataset. To predict whether an aircraft will deviate from its planned entry point, supervised machine learning algorithms are used to train a model. Through experiments on real-world data, we showcase that ASOG provides a systematic way of incorporating the state of the airspace sector and improving the performance of prediction models compared to simple features. The prediction output can be used to notify human air traffic controllers in advance about potential deviation to flight plan upon entry to an airspace sector. This can improve the planning process of air traffic controllers in their work in maintaining safe and efficient air traffic.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Predicting Deviation of Flight Entry into Air Sector using Machine Learning Techniques


    Beteiligte:


    Erscheinungsdatum :

    01.10.2023


    Format / Umfang :

    1645022 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Predicting Sector Complexity Using Machine Learning

    Wieland, Frederick / Rebollo, Juan / Gibbs, Mary et al. | AIAA | 2022


    Predicting Sector Complexity Using Machine Learning

    Wieland, Frederick / Rebollo, Juan / Gibbs, Mary et al. | TIBKAT | 2022


    Prediction of Flight Time Deviation for Lithuanian Airports Using Supervised Machine Learning Model

    Pavel Stefanovič / Rokas Štrimaitis / Olga Kurasova | DOAJ | 2020

    Freier Zugriff

    Flight Delay Prediction Using Machine Learning Techniques

    Tijil, Yash / Dwivedi, Nripendra / Srivastava, Satyam Kumar et al. | IEEE | 2024


    Predicting driver destination using machine learning techniques

    Manasseh, Christian / Sengupta, Raja | IEEE | 2013