Abstract
The future airspace has to provide a reliable infrastructure and operational concept to ensure efficient and safe operations considering both flight-centric operations and the integration of new entrants. We propose an approach for a dynamic sectorization to manage the air traffic demand and flow appropriately. Our dynamic sectorization results in enhancements of the current operational structure (less deviation in controller task load) and leads to a significantly lower controller task load for the newly created airspace. Since future 4D trajectory management demands an efficient consideration of operational (e.g., temporally restricted areas), ecological (e.g., contrail prevention), and economic (e.g., functional airspace blocks) constraints, our dynamic sectorization method contributes to the highly flexible use of current and future airspace. In this paper, we provide an overview of several use cases and describe the working principle of our approach: fuzzy clustering of air traffic, Voronoi diagram for initial structures, and evolutionary algorithms for optimization.
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References
Gerdes I, Temme A, Schultz M (2016) Dynamic airspace sectorization using controller task load. SESAR innovation days, Delft
Standfuß T, Gerdes I, Temme A, Schultz M (2018) Dynamic airspace optimisation. CEAS Aeronaut J
Rosenow J, Fricke H, Luchkova T, Schultz M (2018) Minimizing contrail formation by rerouting around dynamic ice-supersaturated regions. AAOAJ 2(3):105–111
Eurocontrol FAA (2016) Comparison of air traffic management-related operational performance: U.S./Europe 2015
Buxbaum J, Standfuß T (2014) Data envelopment analysis. DFS Innovat Fokus 1:11–16
Regulation (EC) No549/2004, Framework for the creation of the single European sky
Standfuß T, Fichert F, Schultz M (2017) Efficiency gains through functional airspace blocks? An analysis of economies of scale in European air traffic management, ITEA
Button K, Nieva R (2013) Single European sky and the functional airspace blocks: will they improve economic efficiency, vol 33, pp 73–80
Luchkova T, Vujasinovic R, Lau A, Schultz M (2015) Analysis of impacts an Eruption of volcano stromboli could have on European air traffic. In: 11th USA/Europe air traffic management research and development seminar
Kaltenhäuser S, Morlang F, Luchkova T, Hampe J, Sippel M (2017) Facilitating sustainable commercial space transportation through an efficient integration into air traffic management. New Space 5(4):244–256
Luchkova T, Kaltenhäuser S, Morlang F (2016) Air traffic impact analysis design for a suborbital point-to-point passenger transport concept. In: 3rd annual space traffic management conference “Emerging Dynamics”
Kerner BS (1998) Experimental features of self-organization in traffic flow. Phys Rev Lett 81:3797
Sunil E, Hoekstra J, Ellerbroek J, Bussink F, Nieuwenhuisen D, Vidosavljevic A, Kern S (2015) Metropolis: relating airspace structure and capacity for extreme traffic densities. In: 11th USA/Europe air traffic management research and development seminar
Schneider O, Kern S, Knabe F, Gerdes I, Delahaye D, Vidosavljevic A, Leeuwen P van, Nieuwenhuisen D, Sunil E, Hoekstra J, Ellerbroek J (2014) METROPOLIS—urban airspace design, D 2.2—concept design (D 2.2)
Temme A, Helm S (2016) Unmanned freight operations. DLRK 2016, Brunswick
Brodersen Y, Luchkova T, Temme A, Lindner M, Rosenow J, Schultz M (2017) Entwicklung und Bewertung von Formationsflugszenarien unbemannter Frachtflugzeuge. DLRK 2017, Munich
Kopardekar P, Bilimoria K, Sridhar B (2007) Initial concepts for dynamic airspace configuration. In: 7th AIAA aviation technology, integration and operations conference (ATIO), Belfast
Flener P, Pearson J (2013) Automatic airspace sectorisation: a survey. Comput Res Reposit 1311:0653
Keller A (2002) Objective function based fuzzy clustering in air traffic management. Otto-von-Guericke University, Magdeburg
Oliviera JV de, Pedrycz W (2007) Advances in fuzzy clustering and its applications. Wiley
Krishnapuram R, Keller J (1993) A possibilistic approach to clustering. IEEE Trans Fuzzy Syst 2:98–110
Berg M, Cheong O, van Kevald M, Pvermars M (2008) Computational geometry, algorithms and applications. Springer, Berlin, Heidelberg
Michalewicz Z (1996) Genetic algorithms + data structures = evolution programs. Springer, Berlin, Heidelberg
Gerdes I, Klawonn F, Kruse R (2004) Evolutionäre Algorithmen. Vieweg, Wiesbaden
Eurocontrol (2017) Eurocontrol demand data repository. Eurocontrol. https://www.eurocontrol.int/ddr. Accessed 26 Sept 2017
Delahaye D, Schoenauer M, Alliot JM (1998) Airspace sectoring by evolutionary algorithms. In: IEEE international congress on evolutionary computation
Kulkarni S, Ganesan R, Sherry L (2011) Static sectorization approach to dynamic airspace configuration using approximate dynamic programming. In: ICNS conference
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Schultz, M., Gerdes, I., Standfuß, T., Temme, A. (2019). Future Airspace Design by Dynamic Sectorization. In: Electronic Navigation Research Institute (eds) Air Traffic Management and Systems III. EIWAC 2017. Lecture Notes in Electrical Engineering, vol 555. Springer, Singapore. https://doi.org/10.1007/978-981-13-7086-1_2
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DOI: https://doi.org/10.1007/978-981-13-7086-1_2
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