Airport and Terminal Airspace Capacity Planning During Convective Weather Events

Airport capacity planning during periods of convective weather is a critical need to support air traffic management decision-making. This activity often revolves around finding the appropriate Traffic Management Initiatives (TMIs) that balance the demand to a level that matches the operational capacity of the airport and airspace resources involved. Designing TMIs that effectively match the onset, duration and impact of the weather is critical for promoting operational efficiency as poor program design can lead to an under-delivery or over-delivery of flights. In this paper, we propose a method for planning airport capacity during convective weather events. We integrate machine learning based estimates of the en route airspace capacity with a rule-based algorithm that predicts the terminal and airport capacity conditions based on a weather forecast to design TMIs to manage an airport in convective weather. An air traffic simulation is also used to evaluate the operational performance achieved when applying the models. We present a case study examining the impact of using this approach at Philadelphia International Airport over a set of case days and compare the results to those achieved with TMIs that were historically implemented. The performance suggests that the use of such an approach has the potential to provide improved performance relative to historical TMIs.