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Autor(en):
L. Tyburzy, M. Schaper, L. Nöhren, K. Muth
Zusammenfassung:
In order to reach the goals of the Paris Agreement, it has become evident that it is necessary to reduce the impact of the whole aviation sector on the environment. The project GreAT (Greener Air Traffic Operations) aims to showcase how a combination of advanced air traffic management tools and procedures for departure, en-route, arrival and surface operations can support this reduction of aviations environmental impact. For the surface operations of aircraft at the airport, the surface management system TraMICS (Traffic Management Intrusion and Compliance System Plus) has been developed to support ground controllers with a security situation assessment and trajectory advisories for taxi operations. TraMICS uses a genetic algorithm to plan and adjust taxi-trajectories in real time to resolve conflicts between aircraft on the ground, with the aim to reduce holding time after engine startup as well as preventable braking and acceleration actions due to other traffic. This paper presents a case study, comparing different configuration profiles for generating conflict-free trajectories using TraMICS and Hamburg airport topology. By using a trajectory configuration profile with higher penalties for holds during the taxi phase, it was possible to create more efficient taxi trajectories with 80 percent fewer holds.
Veranstaltung:
Deutscher Luft- und Raumfahrtkongress 2023, Stuttgart
Verlag, Ort:
Deutsche Gesellschaft für Luft- und Raumfahrt - Lilienthal-Oberth e.V., Bonn, 2023
Medientyp:
Conference Paper
Sprache:
englisch
Format:
21,0 x 29,7 cm, 6 Seiten
URN:
urn:nbn:de:101:1-2023112212380135836076
DOI:
10.25967/610520
Stichworte zum Inhalt:
Air Traffic Management, Controller Support, Surface Management, Environment
Verfügbarkeit:
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Zitierform:
Tyburzy, L.; Schaper, M.; et al. (2023): Greener Conflict-Free Taxi Trajectories using Genetic Algorithms. Deutsche Gesellschaft für Luft- und Raumfahrt - Lilienthal-Oberth e.V.. (Text). https://doi.org/10.25967/610520. urn:nbn:de:101:1-2023112212380135836076.
Veröffentlicht am:
22.11.2023