Air Traffic Flow Management in the Presence of Uncertainty

Deterministic air traffic flow management (TFM) decisions -the state of the art in terms of implementation- often result in "lost" airspace capacity, because the inherent uncertainties in weather predictions make it difficult to determine the number of aircraft that can be safely accommodated in a volume of airspace during a given period. As a result, there is a distinct need for TFM algorithms that utilize available stochastic weather information for improved decision making. To this end, we first develop a methodology to determine the stochastic capacity for a volume of airspace given the forecast weather and associated uncertainty. Then, we use this information as input to a dynamic stochastic optimization algorithm to determine the number of aircraft to send towards a volume, providing specific guidance for routing aircraft in the presence of the uncertainties of adverse weather.