Optimization of Airport Field Taxi Scheduling Considering Runway Crossing

Airport congestion is a major bottleneck in the global air transportation system, and inefficient ground operations at multi-runway hub airports further intensify this challenge. The complex interactions between arriving and departing flights often lead to extended taxi times, excessive fuel burn, and increased controller workload. To systematically address these issues, this paper proposes an intelligent taxi scheduling model that dynamically optimizes both the routing sequence and timing of aircraft operations. The framework incorporates realistic operational constraints, including runway-crossing strategies and conflict-free requirements within the runway–taxiway network. A multi-objective particle swarm optimization (MOPSO) algorithm is employed to effectively solve the high-dimensional search space with competing objectives. Simulation studies at two major international hub airports in China demonstrate that dynamically selecting the optimal taxiing strategy among “direct crossing,” “stop-and-wait,” and “detour” options can significantly improve ground efficiency, reduce fuel consumption, and lower the frequency of controller interventions. This data-driven approach provides a scalable and practical solution to alleviate airport congestion, offering direct benefits in delay mitigation and the sustainable development of airport surface operations.