The Fuzzy System Sensitivity Analysis: An Example of Air Travel Demand Models

  • Milica Kalić
  • Slavica Dožić
  • Jovana Kuljanin
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 262)


This chapter presents the results obtained by employing both fuzzy logic and sensitivity analysis to model trip generation and trip distribution processes in the domain of air transportation. Qualitative and imprecise information taken from experts represent an invaluable source when objective knowledge on certain process is not available or even does not exist. Thus, fuzzy logic is seen as a convenient mathematical tool that efficiently treats uncertainty in-built in the socio-economic parameters that are selected to describe trip generation and trip distribution problem. The chapter analyzes the sensitivity of fuzzy system solutions obtained by two models in respect to different factors such as domain discretization of input and output variables, various forms of membership function and different approximate reasoning techniques hereby enabling possible improvements to the models.


Sensitivity analysis Fuzzy logic Air travel demand Trip generation Trip distribution 



This research has been supported by the Ministry of Science and Technological Development, Republic of Serbia, as part of the project TR36033 (2011–2014).


  1. 1.
    Arslan, T., Khisty, C.J.: A rational reasoning method from fuzzy perceptions in route choice. Fuzzy Sets Syst. 150(3), 419–435 (2005)CrossRefMATHMathSciNetGoogle Scholar
  2. 2.
    Fleming, K., Ghobrial, A.: An analysis of the determinants of regional air travel demand. Transp. Planning Technol. 18, 37–44 (1994)CrossRefGoogle Scholar
  3. 3.
    Grosche, T., Rothlauf, F., Heinzl, A.: Gravity models for airline passenger volume estimation. J. Air Transp. Manag. 13(4), 175–183 (2007)CrossRefGoogle Scholar
  4. 4.
    Gürocak, H.B., de Sam Lazaro, A.: Fine tuning method for fuzzy logic rule base. Fuzzy Sets Syst. 67(2), 147–161 (1994)Google Scholar
  5. 5.
    Kalić, M.: Fuzzy system sensitivity analysis: An example of trip generation in air transportation. In: Proceedings of the 11th Mini-EURO Conference on Artificial Intelligence in Transportation Systems and Science, and 7th EURO-Working Group Meeting on Transportation, Helsinki, Finland (1999)Google Scholar
  6. 6.
    Kalić, M., Dožić, S., Babić, D.: Predicting Air Travel Demand Using Soft Computing: Belgrade Airport Case Study. 15th Euro Working Group on Transportation, Paris (2012a)Google Scholar
  7. 7.
    Kalić, M., Kuljanin, J., Dožić, S.: Air travel demand fuzzy modelling: Trip generation and trip distribution. WSC17 2012 online conference on soft computing in industrial applications anywhere on earth, 10–21 December 2012 (2012b)Google Scholar
  8. 8.
    Kalić, M., Teodorović, D.: Solving the trip distribution problem by fuzzy rules generated by learning from examples. In: Proceedings of the XXIII Yugoslav Symposium on Operations Research, pp. 777–780. Zlatibor, Yugoslavia, (in Serbian) (1996)Google Scholar
  9. 9.
    Kalić, M., Teodorović, D.: A soft computing approach to trip generation modeling. Paper presented at the 9th Mini EURO Conference Fuzzy sets in traffic and transport systems, Budva, Yugoslavia (1997)Google Scholar
  10. 10.
    Kalić, M., Teodorović, D.: Transportation route choice model using fuzzy inference technique. Transp. Plann. Technol. 26(3), 213–238 (2003)CrossRefGoogle Scholar
  11. 11.
    Kalić, M., Tošić, V.: Soft demand analysis: Belgrade Case Study. In: Proceedings of the 8th Meeting of the Euro Working Group Transportation EWGT and Workshop IFPR on Management of Industrial Logistic Systems “Rome Jubilee 2000 Conference”, pp. 271–275 Rome, Italy (2000)Google Scholar
  12. 12.
    Kaymak, U., van Nauta, L.: A sensitivity analysis approach to introducing weight factors into decision functions in fuzzy multicriteria decision making. Fuzzy Sets Syst. 97(2), 169–182 (1998)Google Scholar
  13. 13.
    Teodorović, D.: Fuzzy logic systems for transportation engineering: the state of the art. Transp. Res. Part A 33, 337–364 (1999)CrossRefGoogle Scholar
  14. 14.
    Teodorović, D., Kalić, M.: Solving the modal split problem by fuzzy rules generated by learning from examples. In: Proceedings of Information Technologies, pp. 48–54. Žabljak, Yugoslavia, (in Serbian) (1996)Google Scholar
  15. 15.
    Teodorović, D., Kikuchi, S.: Transportation route choice model using fuzzy inference technique. In: Ayyub, B.M. (ed.) Proceedings of ISUMA ‘90. The First International Symposium on Uncertainty Modeling and Analysis, pp. 140–145. IEEE Computer Press, College Park, Maryland (1990)Google Scholar
  16. 16.
    Wang, L., Mendel, J.: Generating fuzzy rules by learning from examples. IEEE Trans. Syst. Man Cybern. 22, 1414–1427 (1992)CrossRefMathSciNetGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Milica Kalić
    • 1
  • Slavica Dožić
    • 1
  • Jovana Kuljanin
    • 1
  1. 1.Faculty of Transport and Traffic EngineeringUniversity of BelgradeBelgradeRepublic of Serbia

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