Spatio-temporal graph-based demand and capacity balancing considering flight uncertainties

Abstract

In uncertain environments, Demand and Capacity Balancing (DCB) operations may benefit from shifting focus towards the tactical phase. Currently, air traffic control (ATC) and air traffic flow management (ATFM) operate on different time scales—ATC is tactical, while ATFM is pre-tactical and strategic. Despite these differences, both aim to improve safety and efficiency. Integrating DCB into the tactical phase allows for more responsive management of dynamic conditions. The HYPERSOLVER project, part of the SESAR ER program, is focused on developing and implementing integrated ATFM and ATC methods to address these evolving challenges effectively. In this context, this paper proposes a spatio-temporal graph-based DCB approach to rapidly solve DCB problems in large-scale high-density scenarios considering uncertainty (e.g., flight speed). Simulation experiments based on real European airspace scenarios demonstrate the proposed method can quickly and efficiently solve large-scale DCB problems in high-density scenarios (solving a DCB instance for 2,000 flights in 9.68 seconds, while the rate of changed flights, the average delay time for delayed flights, and the rate of additional flight time for rerouted flights are only 8.46%, 12.2 minutes, and 9.34%, respectively).