Scalable Multidimensional Uncertainty Evaluation Approach to Strategic Air Traffic Flow Management

Convective weather events cause capacity reduction in the National Airspace System (NAS), and lead to traffic congestion. To mitigate congestion, strategic air traffic flow management plans traffic flows at a long look-ahead time (2-15 hour). Planning at this timeframe is challenging, due to the wide possibility of weather events and requirement for real-time management. To conquer these challenges, we need an approach to quickly assess the impact of predicted weather events on the performance of air traffic system. In this paper, we use a scalable multidimensional uncertainty evaluation approach, called M-PCM-OFFD, to address this problem. Simulation studies show the effectiveness of this approach for the performance evaluation of air traffic system. In addition, we investigate further capability of M-PCM-OFFD through exploring higher-level OFFDs. Finally, we introduce an uncertainty-exploiting framework to enable real-time strategic air traffic management under weather uncertainty.