Taxi-out time prediction is one of the key elements required for successful implementation of Airport Collaborative Decision Making (A-CDM) system. In this paper, we discuss an application of a Machine Learning technique called Random Forest algorithm to predict taxi-out time. Using information retrieved from Incheon International airport’s A-CDM and METAR databases, the model is trained and tested. This paper presents the process of selecting explanatory variables (i.e., features) using feature importance and demonstrates the prediction accuracy of the resulting model.


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    Title :

    Taxi-Out Time Prediction at a Busy Airport using Random Forest Algorithm


    Contributors:
    Kim, Jihoon (author) / Baik, Hojoing (author)


    Publication date :

    2021-10-03


    Size :

    1719660 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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