This paper proposes a method to dynamically control a buffer that determines ground delay times in order to improve the efficiency of air traffic flow management. When arrival demand at an airport exceeds the capacity of the airport, airborne delay occurs in the airspace around the airport. To resolve the imbalance between demand and capacity at the airport, flights that are expected to be airborne-delayed are given a ground delay before departure. A buffer is used to calculate the ground delay time and the buffer is basically fixed in the current operation. We developed a dynamic buffer algorithm based on deep reinforcement learning to improve operational efficiency by changing the buffer based on the uncertainty characteristics of each flight. Numerical simulations were performed in an environment consisting of multiple routes with different uncertainties. The simulation results showed that compared with the conventional fixed buffer algorithm, the proposed method could improve the operational efficiency in terms of airborne delay and throughput loss while balancing them.
Sato et al. (Thu,) studied this question.