This paper describes an Artificial Intelligence (AI) Deep Reinforcement Learning (DRL) model application to airport surface movement planning. Specific areas of support include planning conflict-free paths of flights, advising flight actions to avoid conflicts, and meeting flight-specific time constraints. We describe a unique way to represent the surface operational environment and schedule constraints for leveraging the DRL model with a Convolutional Neural Network and also demonstrate the training process and performance for routing surface traffic at a hypothetical airport. The proposed model learns entirely from the simulation to make sequential decisions for finding conflict-free routes, addressing incomplete schedule information, and coordinating the simultaneous rerouting of multiple flights. Such a DRL model, once adapted and trained for a realistic airport, may advise controllers on surface route planning, conflict probing, and resolving predicted conflicts.


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    Title :

    Deep Reinforcement Learning Applied to Airport Surface Movement Planning


    Contributors:
    Tien, Shin-Lai Alex (author) / Tang, Huang (author) / Kirk, Daniel (author) / Vargo, Erik (author) / Liu, Sheng (author)


    Publication date :

    2019-09-01


    Size :

    2018957 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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