Probabilistic Airspace Congestion Management

Abstract

*† ‡ This paper presents a novel approach to airspace congestion management, in which prediction uncertainties are measured and explicitly applied to improve decision-making. The planned automation system would continuously monitor airspace conditions, and identify areas of potential congestion. Airspace users are alerted if their flights are planned through congested areas, can take preventive measures, or provide alternate route options. If congestion probabilities continue to rise, the system suggests minimal corrective actions, such as flight-specific ground delays or reroutes, to maintain an acceptable level of congestion risk. In this way, both safe traffic levels and high throughput can be maintained.