Accurate estimation of airspace capacity is essential to a safe, efficient and predictable air transportation system. Conventional approaches focus on controller workload using airspace complexity measurements that only consider operational conditions of controllers. However, such model-driven methods don’t completely demonstrate airspace capacity in the real world because of lack of consideration for other critical factors such as weather. To address this challenge, we propose a new airspace capacity estimation model based on decision tree ensembles. Our model combines multi-source data to quantify the maximum transportation capacity of en route sector under different circumstances.This paper makes the following contributions: (a) we present an interpretable data-driven model that estimates the capacities of the National Airspace System (NAS), and highlight factor importance for airspace capacities; (b) the airspace capacity estimated by our proposed model is dynamically adjusted based on the real-time environment that has the potential to be a guide for temporary flight path changes or air traffic selections for an emergency landing; and (c) we promote the role of machine learning-based methods in future ATM and airspace optimization.


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    Title :

    Tree-Based Airspace Capacity Estimation


    Contributors:
    Zhang, Kai (author) / Liu, Yongxin (author) / Wang, Jian (author) / Song, Houbing (author) / Liu, Dahai (author)


    Publication date :

    2020-09-01


    Size :

    1257647 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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