Accurate taxi-out time predictions are a valuable asset in enabling efficient runway scheduling in real-time operations so as to reduce taxi-out times and fuel consumption on the airport surface. This paper will focus on how the neural networks, regression tree, reinforcement learning, and multilayer perceptron methods can be used for predicting taxi-out time. These four methods are assessed based on their performance indicators, applied on Charles de Gaulle operational taxi data and benchmarked against real-life taxi-out time profiles. The root-mean-squared error metric is chosen as the most important performance indicator, which gives, for the applied regression tree method, on any given day, an average error of 1.6 min. The regression tree turns out to be the most efficient method, which is then subsequently applied in a case study for predicting the taxi-out time and finding the key-related precursors extracted from the top 10 features.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Taxi-Out Time Prediction Model at Charles de Gaulle Airport


    Beteiligte:
    Herrema, Floris (Autor:in) / Curran, Richard (Autor:in) / Visser, Hendrikus (Autor:in) / Huet, Denis (Autor:in) / Lacote, Régis (Autor:in)

    Erschienen in:

    Erscheinungsdatum :

    01.03.2018




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




    Charles de Gaulle Airport surface radar

    Roche, Jean Pierre | SLUB | 1975


    TGV-Bahnhof am Airport Charles de Gaulle

    Rossberg, Ralf Roman | IuD Bahn | 1994


    The CDGVAL at Paris - Charles de Gaulle airport

    Russell Publishing Ltd. Court Lodge, Hogtrough Hill Hogtrough Hill, Brasted GB - TN16 1NU Kent | IuD Bahn | 2007


    Rail links with the Roissy - Charles-de-Gaulle Airport hub

    Leboeuf, M. | British Library Online Contents | 2001