Tactical demand and capacity balancing using incremental search in spatio-temporal graphs with flight uncertainty

Demand and Capacity Balancing (DCB) operations, typically implemented pre-flight, face limitations in effectiveness due to uncertainties during airspace operations. Therefore, executing DCB during the tactical phase (as close to the departure time as possible) holds promise for better addressing these uncertainties. This study proposes a tactical-phase DCB method that accounts for uncertainties to meet practical application scenarios and requirements: compatibility with dynamic environments, high-speed computation, fairness and transparency, and high customisability. The large-scale tactical DCB problem is transformed into a hotspot-free trajectory planning problem based on sequential planning to accommodate stakeholders’ diverse performance preferences. An adaptive directed spatio-temporal graph method is introduced, enabling the integration optimisation of multiple Air Traffic Flow Management (ATFM) measures (ground delay, rerouting, and speed control) while considering flight uncertainties and fuel consumption constraints. A Heterogeneous Multi-Objective Incremental A${}^{*}$ (HMOIA${}^{*}$) path search method is also developed to significantly accelerate problem-solving and meet tactical operational demands, ensuring optimal solutions by designing an admissible heuristic function. Simulation experiments based on historical European data demonstrate that the proposed method can resolve all overloaded air traffic service units with acceptable arrival delay time and fuel consumption. Compared to the Computer-Assisted Slot Allocation (CASA) method currently used in European operations, the proposed approach reduces the number of delayed flights and average delay time by approximately 79.4 % and 92.1 %, respectively. The proposed method demonstrates its value for further development to explore its potential as an upgrade to the CASA method in real-world operations.