Analyzing and Modeling Capacity for Decentralized Air Traffic Control

The current system of Air Traffic Control (ATC) relies on a centralized control architecture. At its core, this system is heavily dependent on manual intervention by human Air Traffic Controllers (ATCos) to ensure safe operations. The capacity of this system is, therefore, closely tied to the maximum workload that can be tolerated by ATCos. Although this system has served the needs of the air transportation industry thus far, the increasing delays and congestion reported in many areas indicates that the current centralized operational model is rapidly approaching saturation levels. To cope with the expected future increases of traffic demand, many researchers have proposed a transition to a decentralized traffic separation paradigm in en route airspaces. Although there are several variants of decentralized ATC, this thesis focuses on a variant known as self-separation. In self-separated airspace, each individual aircraft is responsible for its own separation with all surrounding traffic. To facilitate self-separation, significant research effort has been devoted towards the development of new algorithms for automated airborne Conflict Detection and Resolution (CD&R). However, in spite of over two decades of active research highlighting its theorized benefits, decentralization/self-separation is yet to be deployed in the field. From a technical point of view a lack of understanding on three open issues namely airspace design, airspace safety modeling, and airspace capacity modeling, have impeded its further development and implementation. The goal of this research is to address these three open problems in order to bring self-separated ATC closer to reality. Consequently, the main body of this thesis is divided into three parts, with each part tackling one of the three aforementioned open problems...