Network-level aircraft trajectory planning via multi-agent deep reinforcement learning: Balancing climate considerations and operational manageability

Optimizing flight trajectories emerges as a viable strategy to mitigate the non-CO2 climate impacts of aviation. However, integrating individually optimized trajectories into the air traffic management system poses operational challenges, notably in terms of traffic safety and complexity. This paper...

Ausführliche Beschreibung

Bibliographische Detailangaben

Veröffentlicht in:	Expert systems with applications Jg. 271; S. 126604
Hauptverfasser:	Baneshi, Fateme, Cerezo-Magaña, María, Soler, Manuel
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier Ltd 01.05.2025
Schlagworte:	Air traffic management system Aircraft trajectory planning Aviation climate impact Conflict resolution Multi-agent deep reinforcement learning Twin delayed deep deterministic policy gradient algorithm Multi-agent deep reinforcement learning Air traffic management system Aviation climate impact Aircraft trajectory planning Conflict resolution Twin delayed deep deterministic policy gradient algorithm
ISSN:	0957-4174
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!