This paper presents a framework aimed at monitoring the behavior of aircraft in a given airspace. Trajectories that constitute typical operations are determined and learned using data-driven methods. Standard procedures are used by air traffic controllers (ATCs) to guide aircraft, ensure the safety of the airspace, and maximize runway occupancy. Even though standard procedures are used by ATCs, control of the aircraft remains with the pilots, leading to large variability in the flight patterns observed. Two methods for identifying typical operations and their variability from recorded radar tracks are presented. This knowledge base is then used to monitor the conformance of current operations against operations previously identified as typical. A tool called AirTrajectoryMiner is presented, aiming at monitoring the instantaneous health of the airspace, in real time. The airspace is “healthy” when all aircraft are flying according to typical operations. A measure of complexity is introduced, measuring the conformance of current flight to typical flight patterns. When an aircraft does not conform, the complexity increases as more attention from ATC is required to ensure safe separation between aircraft.

The introduction of new technologies and concepts of operation in the air transportation system is not possible unless they can be proven not to adversely affect the system operation under not only nominal but also degraded conditions. In extreme scenarios, degraded operations due to partial or complete technological failures should never endanger system safety. Many past system evolutions, whether ground-based or airborne, have been based on trial and error, and system safety was addressed only after a specific event yielded dramatic or near-dramatic consequences. Future system evolutions, however, must leverage available computation, prior knowledge, and abstract reasoning to anticipate all possible system degradations and prove that such degradations are graceful and safe. This paper is concerned with the graceful degradation of high-density structured arrival traffic against partial or complete surveillance failures. It is shown that for equal performance requirements, some traffic configurations might be easier to handle than others, thereby offering a quantitative perspective on these traffic configurations' ability to ldquogracefully degrade.rdquo To support our work, we also introduce a new conflict resolution algorithm aimed at solving conflicts involving many aircraft when aircraft position information is in the process of degrading.