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This paper presents an approach for creating common weather avoidance reroutes for multiple flights and the associated benefits analysis, which is an extension of the single flight advisories generated using the Dynamic Weather Routes (DWR) concept. These multiple flight advisories are implemented in the National Airspace System (NAS) Constraint Evaluation and Notification Tool (NASCENT), a nation-wide simulation environment to generate time-and fuel-saving alternate routes for flights during severe weather events. These single flight advisories are clustered together in the same Center by considering parameters such as a common return capture fix. The clustering helps propose routes called, Multi-Flight Common Routes (MFCR), that avoid weather and other airspace constraints, and save time and fuel. It is expected that these routes would also provide lower workload for traffic managers and controllers since a common route is found for several flights, and presumably the route clearances would be easier and faster. This study was based on 30-days in 2014 and 2015 each, which had most delays attributed to convective weather. The results indicate that many opportunities exist where individual flight routes can be clustered to fly along a common route to save a significant amount of time and fuel, and potentially reducing the amount of coordination needed.

Airspace capacity reduction due to convective weather impedes air traffic flows and causes traffic congestion. This study presents an algorithm that reroutes flights in the presence of winds, enroute convective weather, and congested airspace based on stochastic dynamic programming. A stochastic disturbance model incorporates into the reroute design process the capacity uncertainty. A trajectory-based airspace demand model is employed for calculating current and future airspace demand. The optimal routes minimize the total expected traveling time, weather incursion, and induced congestion costs. They are compared to weather-avoidance routes calculated using deterministic dynamic programming. The stochastic reroutes have smaller deviation probability than the deterministic counterpart when both reroutes have similar total flight distance. The stochastic rerouting algorithm takes into account all convective weather fields with all severity levels while the deterministic algorithm only accounts for convective weather systems exceeding a specified level of severity. When the stochastic reroutes are compared to the actual flight routes, they have similar total flight time, and both have about 1% of travel time crossing congested enroute sectors on average. The actual flight routes induce slightly less traffic congestion than the stochastic reroutes but intercept more severe convective weather.

Given the numerous sources of uncertainty inherent in the National Airspace System, plans to alleviate demand-capacity imbalances are sometimes untrustworthy. In addition, differing solutions to demand-capacity imbalances are difficult to compare in terms of their potential quality in the face of weather and schedule uncertainties. In this work, a method for evaluating the robustness of a traffic scheduling solution to various uncertainties is presented. By converting solutions to predicted demands and capacities of sectors and airports together with measures of uncertainty on those predictions, any given plan can be evaluated based on the number of expected capacity violations along with distributions on their severity. With such measures, the most robust of a set of potential plans might be chosen by a traffic manager. As a side-effect of this approach, the value of reduced uncertainties can be measured in terms of reduced expected violations. The value of reduced uncertainty is demonstrated using a deterministic, minimum-delay approach to managing demand-capacity imbalances. When the deterministic solution is measured in terms of expected violations given models of demand and capacity uncertainty, results indicate that if the calculated uncertainty measures are reduced by 50%, then the number of expected violations will decrease by just over 40%.