Determining Stochastic Airspace Capacity for Air Traffic Flow Management

Abstract

Deterministic air traffic flow management (TFM) decisions—the state of the art in terms of implementation—often result in unused airspace capacity. This is because the inherent uncertainties in weather predictions make it difficult to determine the number of aircraft that can be safely accommodated in a region of airspace during a given period. On the other hand, stochastic TFM algorithms are not amenable to implementation in practice due to the lack of valid stochastic mappings between weather forecasts and airspace capacity to serve as inputs to these algorithms. To fill this gap, we develop a fast simulation-based methodology to determine the stochastic capacity of a region of airspace using integrated weather-traffic models. The developed methodology consists of combining ensemble weather forecast information with an air traffic control algorithm to generate capacity maps over time. We demonstrate the overall methodology through a novel conflict resolution procedure and a simple weather scenario generation tool, and also discuss the potential use of ensemble weather forecasts. An operational study based on comparisons of the generated capacity distributions with observed impacts of weather events on air traffic is also presented.