This paper describes the development of an automated mechanism to alert aviation traffic managers of the need to take action to adjust the rate of aircraft arriving into airports. When rates are too high, air traffic controllers are forced to do costly maneuvering and to hold aircraft to maintain required spacing. When arrival rates are too low, valuable airport landing capacity goes unused. In today's operations, mismatches between the planned and actual arrival rates often occur gradually and may not even be noticed until too late, after significant problems have materialized. This paper proposes an alerting mechanism that uses realtime signal metrics based on actual airspace operations to alert controllers of impending problems. Alerts would be triggered when signal metrics crossed their respective threshold values, which would be tailored for specific airspaces and generated with sufficient lead time to allow for mitigating actions. The alerting mechanism would reduce reliance on manual monitoring and thus reduce traffic manager workload. With historical flight data from airspace surrounding Atlanta International Airport an initial predictive model was developed and validated for one possible signal metric. Through discussions with subject matter experts, an analysis of various metric threshold values was also performed.

Severe en route weather is one of the major challenges for both Federal Aviation Administration (FAA) airspace managers and for airline and other airspace users. Uncertainty associated with changing weather patterns and severity, coupled with uncertainty in how airlines and other aircraft operators will react to the changing weather creates a significant challenge for traffic managers (TMs). TMs must decide, with limited information, how best to handle likely imbalances between available airspace capacity that will change over time due to dynamic weather conditions and air traffic demand for that airspace which also is changing over time as different aircraft operators seek to best meet their respective business needs. A planned enhancement to the traffic management automation system, the Collaborative Airspace Congestion Resolution (CACR) capability allows TMs to effectively and efficiently manage airspace congestion in a tactical time frame (0-2 hours). CACR has four key components: it predicts sector demand and its associated uncertainty; it predicts sector capacity including the impact of weather; it identifies the problem; and, it generates congestion resolution plans. The purpose of the analysis was to determine the benefits of using the CACR capability.The benefits analysis was performed by assessing the reduced flight and ground delays achieved by using the capability in a severe weather situation which also occurred in the tactical timeframe. The approach for estimating the benefits of CACR was to rerun two historical bad-weather days in the NAS, and to create a situation in which the analysts played the role of TM to solve the problem of excess air traffic demand in light of weather-impacted sector capacities. Two simulated runs were performed for each day, with one simulating today's operations using playbooks for rerouting and the other one simulating the future by utilizing the CACR capability. The benefits were determined by calculating the difference of the ground delay and flight time for each simulated run.