Air Traffic Incidents Analysis with the Use of Fuzzy Sets

  • Michał Lower
  • Jan Magott
  • Jacek Skorupski
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7894)


In safety, reliability as well as risk analysis and management, information often is uncertain and imprecise. The approach to air incident analysis under uncertain and imprecise information presented in our paper is inspired by the possibility theory. Notably, in such analyses these are both: static and dynamic components that have to be included. As part of this work, static analysis of a serious incident has been performed. In order to do this, probability scale which is based on fuzzy set theory has been given. The scenarios of transformation of incident into accident have been found and their fuzzy probabilities have been calculated. Finally, it has been shown that elimination of one of premises for transformation of the incident into accident significantly reduces the possibility of this transformation.


serious incident fuzzy probability events tree fuzzy inference air traffic safety 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Aven, T., Zio, E.: Some considerations on the treatment of uncertainties in risk assessment for practical decision making. Reliability Engineering and System Safety 96, 64–74 (2011)CrossRefGoogle Scholar
  2. 2.
    Babczyński, T., Łukowicz, M., Magott, J.: Time coordination of distance protections using probabilistic fault trees with time dependencies. IEEE Transaction on Power Delivery 25(3), 1402–1409 (2010)CrossRefGoogle Scholar
  3. 3.
    Blom, H., Corker, K., Stroeve, S.: On the integration of human performance and collision risk simulation models of runway operation. National Aerospace Laboratory NLR, Report NLR-TP-2006-682 (2006)Google Scholar
  4. 4.
    Brooker, P.: Air Traffic Management accident risk. Part 1: The limits of realistic modelling. Safety Science 44, 419–450 (2006)CrossRefGoogle Scholar
  5. 5.
    Civil Aviation Authority, Air Traffic Safety Requirements CAP 670, CAA Safety Regulation Group (2012)Google Scholar
  6. 6.
    Kacprzyk, J.: Fuzzy Sets in Systems Analysis. National Scientific Publishers, Warsaw (1986) (in Polish)Google Scholar
  7. 7.
    Kenarangui, R.: Event-tree analysis by fuzzy probability. IEEE Transactions on Reliability 40(1), 120–124 (1991)zbMATHCrossRefGoogle Scholar
  8. 8.
    Klich, E.: Flight Safety in Air Transport, Exploitation Problems Library, Exploitation Technology Institute - PIB, Radom (2011) (in Polish)Google Scholar
  9. 9.
    Magott, J., Skrobanek, S.: Timing analysis of safety properties using fault trees with time dependencies and timed state-charts. Reliability Engineering and Systems Safety 97(1), 14–26 (2012)CrossRefGoogle Scholar
  10. 10.
    Onisawa, T., Kacprzyk, J. (eds.): Reliability and Safety Analysis under Fuzziness. Physica-Verlag, Springer, Heidelberg (1995)Google Scholar
  11. 11.
    Rajati, M.R., Mendel, J.M., Wu, D.: Solving Zadeh’s Magnus challenge problem on linguistic probabilities via linguistic weighted averages. In: IEEE Int. Conf. Fuzzy Systems, FUZZ, June 27-30 (2011)Google Scholar
  12. 12.
    Rutkowska, D., Piliński, M., Rutkowski, L.: Neural Networks, Genetic Algorithms and Fuzzy Systems. Scientific Publishers PWN, Warsaw-Lodz (1997) (in Polish)Google Scholar
  13. 13.
    Safety Management Manual (SMM), 1st edn. International Civil Aviation Organization, Doc 9859, AN/460 (2006) Google Scholar
  14. 14.
    Shortle, J., Xie, Y., Chen, C., Donohue, G.: Simulating Collision Probabilities of Landing Airplanes at Non-towered Airports. Transactions of the Society for Computer Simulation 79(10), 1–17 (2003)Google Scholar
  15. 15.
    Skorupski, J.: Method of analysis of the relation between serious incident and accident in air traffic. In: Berenguer, Grall, Soares (eds.) Advances in Safety, Reliability and Risk Management, pp. 2393–2401. Taylor & Francis Group (2012)Google Scholar
  16. 16.
    Tanaka, H., Fan, L.T., Lai, F.S., Toguchi, K.: Fault-tree analysis by fuzzy probability. IEEE Transactions on Reliability 32(5), 453–457 (1983)zbMATHCrossRefGoogle Scholar
  17. 17.
    Tyagi, S.K., Pandey, D., Tyagi, R.: Fuzzy set theoretic approach to fault tree analysis. International Journal of Engineering, Science and Technology 2(5), 276–283 (2010)CrossRefGoogle Scholar
  18. 18.
    Urząd Lotnictwa Cywilnego (Civil Aviation Authority of the Republic of Poland): Statement No. 78 of President of Civil Aviation Authority from 18th of September 2009 on air event No. 344/07, Warszawa (2009) (in Polish) Google Scholar
  19. 19.
    Weather Underground Internet Service,

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Michał Lower
    • 1
  • Jan Magott
    • 1
  • Jacek Skorupski
    • 2
  1. 1.Faculty of ElectronicsWrocław University of TechnologyWrocławPoland
  2. 2.Faculty of TransportWarsaw University of TechnologyWarsawPoland

Personalised recommendations