Due to increasing passenger and flight numbers, airports need to plan and schedule carefully to avoid wasting their resources, but also congestion and missed flights. In this paper, we present a deep learning framework for predicting the number of passengers arriving at an airport within a 15-min interval. To this end, a first neural network predicts the number of passengers on a given flight. These results are then being used with a second neural network to predict the number of passengers in each interval.


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    Titel :

    Predictive Analytics in Aviation Management: Passenger Arrival Prediction


    Weitere Titelangaben:

    Operations Research Proceedings


    Beteiligte:
    Neufeld, Janis S. (Herausgeber:in) / Buscher, Udo (Herausgeber:in) / Lasch, Rainer (Herausgeber:in) / Möst, Dominik (Herausgeber:in) / Schönberger, Jörn (Herausgeber:in) / Moll, Maximilian (Autor:in) / Berg, Thomas (Autor:in) / Ewers, Simon (Autor:in) / Schmidt, Michael (Autor:in)


    Erscheinungsdatum :

    25.09.2020


    Format / Umfang :

    8 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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