Dynamic Airborne Delay Buffer Selection for Efficient Air Traffic Flow Management

Abstract

Air traffic flow management balances strategically demand and capacity by applying various initiatives such as ground delay programs and controlled enroute delays. The delay assigned to each flight is determined by the estimated time of arrival and the maximum allowed airborne delay (buffer) set to absorb uncertainties and minimize arrival runway throughput and capacity loss. Current operations often use a constant buffer regardless of the projected traffic. This research uses high-fidelity traffic simulations to investigate the effect of a dynamically-selected buffer optimizing the daily flow. Three metrics are introduced to measure the performance- ground delay, airborne delay and capacity loss. Simulations over 162 days of traffic show the potential for considerable savings using the proposed method. Furthermore, initial feasibility investigation of machine learning applied to the dynamic buffer selection problem is performed and it is concluded that despite a certain loss of optimality and estimation accuracy challenges, such techniques can be potentially used in real-life implementation.