Dynamic airspace optimisation


Airspace analysis indicates that besides operational and regulative frameworks, a causal relationship between the kind of airspace sector concept and inefficiency of flight guidance also exists. Today, flow follows an established airspace structure but in future, a more flight-centred view will lead to a reversed approach: the structure has to adapt to dynamic air traffic requirements. Therefore, a new approach has been developed that allows the proposition of fundamental structuring based on a variety of assessment criteria. Thereby, genetic algorithms are used to optimise the proposed structures. The selected optimisation not only is able to guarantee smooth transitions between structures but also takes work load of air traffic controllers into account.

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  1. 1.

    Standfuß, T., Buxbaum, J.: Data envelopment analysis—Vorschlag einer neuen Methodik zum Performancevergleich der Flugsicherungen. In: Innovation im Fokus, pp. 11–16. DFS Deutsche Flugsicherung GmbH, Langen, Germany (2014)

    Google Scholar 

  2. 2.

    European Commission: Regulation (EC) No 549/2004 of the European Parliament and of the Council of 10 March 2004 laying down the framework for the creation of the Single European Sky (2004)

  3. 3.

    European Commission: Regulation (EC) no. 1070/2009. Off. J. Eur. Union (2009)

  4. 4.

    Standfuß, T.: Multidimensional benchmarking of air navigation service providers and application in functional airspace block and sector group context. In: ITEA Conference, Oslo (2015)

  5. 5.

    Button, K., Nieva, R.: Single European Sky and the functional airspace blocks: will they improve economic efficiency? J. Air Transp. Manag. 33, 73–80 (2013)

    Article  Google Scholar 

  6. 6.

    Delahaye, D., Schoenauer, M., Alliot, J.M.: Airspace sectoring by evolutionary algorithms. IEEE Int. Congr. Evol. Comput. (1998)

  7. 7.

    Sherali, H.D., Hill, J.M.: Configuration of airspace sectors for balancing air traffic controller workload. In: Chaovalitwongse, W.A., Furman, K.C. (eds.) Annals of operations research, vol. 203, pp. 3–31, Springer, Berlin (2011)

    Google Scholar 

  8. 8.

    Li, J., Wang, T., Savai, M., Hwang, I.: Graph-based algorithm for dynamic airspace configuration. J. Guid. Control Dyn. 33, 3–31 (2010)

    Article  Google Scholar 

  9. 9.

    Zelinski, S., Lai, C.F.: Comparing methods for dynamic airspace configuration. In: 30th Digital Avionics Systems Conference (2011)

  10. 10.

    Segreva, M., Delahaye, D.: 3D airspace sector design by genetic algorithms. In: MT-ITS, Budapest (2015)

  11. 11.

    Servai, M.P., et. al.: Multilevel graph partitioning algorithm for dynamic airspace configuration. In 10th AIAA Aviation Technology, Integration, and Operations (ATIO) Conference (2010)

  12. 12.

    Coelli, T.J., Rao, P.D., O’Donnell, C.J., Battese, G.E.: An Introduction to Efficiency and Productivity Analysis. Springer, Berlin (2005)

    MATH  Google Scholar 

  13. 13.

    Eurocontrol: Eurocontrol DDR. Eurocontrol [online]. http://www.eurocontrol.int/services/ddr (2017). Accessed 27 Jan 2017

  14. 14.

    Krishnapuram, R., Keller, J.: A possibilistic approach to clustering. IEEE Trans. Fuzzy Syst. 2, 98–110 (1993)

    Article  Google Scholar 

  15. 15.

    Berg, M., Cheong, O., van Kevald, M., Pvermars, M.: Computational Geometry, Algorithms and Applications. Springer, Berlin (2008)

    Google Scholar 

  16. 16.

    Michalewicz, Z.: Genetic Algorithms + Data Structures = Evolution Programs. Springer, Berlin (1996)

    Book  MATH  Google Scholar 

  17. 17.

    Gerdes, I., Temme, A., Schultz, M.: Dynamic Airspace Sectorization Using Controller Task Load. SESAR Innovation Days, Delft (2016)

    Google Scholar 

  18. 18.

    Meinecke, M.: Entwicklung und Evaluation von Lotsenarbeitsbelastungsmodellen in einer Schnellzeitsimulationsumgebung. DLR, Braunschweig (2014)

    Google Scholar 

  19. 19.

    Blondiau, T., Delhaye, E., Adler, N., Proost, S.: Productivity Measurement of Air Traffic Management in Europe. COMPAIR, Redditch (2016)

    Google Scholar 

  20. 20.

    Standfuß, T.: Effizienz und Skaleneffekte europäischer Flugsicherungen. Master Thesis, Technical University, Dresden (2013)

  21. 21.

    Ahmad, etal, Data envelopment analysis and tobit analysis for firm efficiency in perspective of working capital management in manufacturing sector of Pakistan. Int. J. Econ. Financ. Issues (2017)

  22. 22.

    Keller, A.: Objective Function Based Fuzzy Clustering in Air Traffic Management. Otto-von-Guericke University, Magdeburg (2002)

    Google Scholar 

  23. 23.

    de Oliviera, J.V., Pedrycz, W.: Advances in Fuzzy Clustering and Its Applications. Wiley, Hoboken (2007)

    Google Scholar 

  24. 24.

    Gerdes, I., Klawonn, F., Kruse, R.: Evolutionäre Algorithmen. Vieweg, Berlin (2004)

    Book  MATH  Google Scholar 

  25. 25.

    Gerdes, I., Schaper, M.: Management of time based taxi trajectories coupling departure and surface management systems. In: 11th ATM Seminar, Lisboa (2015)

  26. 26.

    Xue, M.: Three dimensional sector design with optimal number of sectors. J. Guid. Control Dyn. 35(2), 609–618 (2012)

    MathSciNet  Article  Google Scholar 

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Standfuß, T., Gerdes, I., Temme, A. et al. Dynamic airspace optimisation. CEAS Aeronaut J 9, 517–531 (2018). https://doi.org/10.1007/s13272-018-0310-7

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  • Air navigation services
  • Airspace
  • Dynamic sectorisation
  • AutoSec