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, a...

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Vydáno v:IEEE access Ročník 13; s. 180232 - 180247
Hlavní autoři: Lian, Guan, Feng, Jingyue, Li, Wenyong, Zhang, Yaping, Yang, Hao
Médium: Journal Article
Jazyk:angličtina
Vydáno: Piscataway IEEE 2025
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Air traffic control
Air transportation
Air transportation industry
Aircraft
aircraft taxiing scheduling
Airports
Atmospheric modeling
Congestion
Controllers
Energy consumption
Fuel consumption
Fuels
Ground operations
multi-objective particle swarm algorithm
multi-runway airport
Multiple objective analysis
Optimization
Particle swarm optimization
Public transportation
Runways
Safety
Scheduling
Spatiotemporal phenomena
Surface treatment
Sustainable development
Taxiing
taxiway strategy
Transportation systems
ISSN:2169-3536, 2169-3536
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Popis
Shrnutí: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.
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ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2025.3622145