Incremental, Probabilistic Decision Making for En Route Traffic Management

En route airspace congestion, often due to convective weather, causes system-wide delays and disruption in the U.S. National Airspace System (NAS). Today's methods for managing congestion are mostly manual, based on uncertain forecasts of weather and traffic demand, and often involve rerouting or delaying entire flows of aircraft. A new, incremental decision-making approach is proposed, in which prediction uncertainty is explicitly used to develop effective and efficient congestion resolution actions. Decisions are made based on a quantitative evaluation of the expected delay cost distribution, and resolution actions are targeted at specific flights, rather than flows. A massively-parallel simulation of the proposed method has been developed, and results for an operational-scale congestion problem are presented.