Tree-Based Airspace Capacity Estimation

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.