Advertisement

Transportation

, 36:97 | Cite as

A hybrid model of fuzzy and AHP for handling public assessments on transportation projects

  • Turan ArslanEmail author
Article

Abstract

Having an effective public participation in transportation planning and project development processes has been a major concern for developed countries. In the United States, for instance, all state Departments of Transportation are subject to the Transportation Equity Act (TEA-21) that formally requires public involvement in transportation planning. Since transportation planning involves public resources and values, judgments by the public should play a key role in determining final decisions. Therefore, all these agencies are required not only to disseminate information to the public, but also to solicit and consider public opinion in forming transportation policy. This work presents a decision support model, with public involvement and public oversight, to help policy makers select appropriate transportation projects for implementation. Since focus groups will face multiple objectives and inexact information in the process, a hybrid model of fuzzy logic and analytical hierarchy process (AHP) is proposed. A set of ‘if–then’ rules based on Weber’s psycho-physical law of 1834 is presented to reason from fuzzy numbers to capture essential subjective preferences, pairwise, among the alternatives. The AHP is then incorporated to estimate preference allotments among alternatives. An example application of the suggested method is provided seeking public approval of an appropriate public bus transportation system choosing between one run by municipal authorities and one run by private agencies to show how this procedure works.

Keywords

AHP Decision support system Fuzzy system Public participation Transportation planning 

Notes

Acknowledgement

The author would like to express his gratitude to Dr. Murat Karacasu (Osman Gazi University, Eskisehir, Turkey) for providing the data used in this work.

References

  1. Arnstein, S.R.: A ladder of citizen participation. J. Am. Plann. Assoc. 35, 216–224 (1969)CrossRefGoogle Scholar
  2. Arslan, T., Khisty, C.J.: A rational reasoning method from fuzzy perceptions in route choice. Fuzzy Sets Syst. 150, 419–435 (2005)CrossRefGoogle Scholar
  3. Avineri, E., Prashker, J., Ceder, A.: Transportation projects selection process using fuzzy sets theory. Fuzzy Sets Syst. 116, 35–47 (2000)CrossRefGoogle Scholar
  4. Choirat, C., Seri, R.: Analytic Hierarchy Process, A Psychometric Approach. ACSEG (Approaches Connexionnistes en Economie et Sciences de Gestion), Huitieme Recontre Internationale, Rennes, France (2001)Google Scholar
  5. DETR: A New Deal for Transport: Better for Everyone. HMSO, London (1998)Google Scholar
  6. Forman, E.H., Gass, S.I.: The analytic hierarchy process—an exposition. Oper. Res. 49(4), 469–486 (2001)CrossRefGoogle Scholar
  7. Karacasu, M.: A decision supportive model proposal for privatization of public bus transportation: Eskisehir sample. Ph.D. Thesis, Istanbul Technical University (2003)Google Scholar
  8. Khisty, C. J.: Investigation of the heuristic processes of wayfinding for non-motorized transportation modes. In: Transportation Research Board 78th Annual Meeting. National Research Council, Washington, D.C. (1999)Google Scholar
  9. Khisty, C.J., Mohammadi, J.: Fundamentals of Systems Engineering with Economics, Probability, and Statistics. Prentice Hall, NJ (2001)Google Scholar
  10. Lootsma, F.: Scale sensitivity in a multiplicative variant of the AHP and SMART. J. Multicriteria Decis. Anal. 2, 87–110 (1993)CrossRefGoogle Scholar
  11. Lootsma, F.: Multicriteria decision analysis in a decision tree. Eur. J. Oper. Res. 101(3), 442–451 (1997a)CrossRefGoogle Scholar
  12. Lootsma, F.A.: Fuzzy Logic for Planning and Decision Making. Kluwer Academic Publishers, Dordrecht, The Netherlands (1997b)Google Scholar
  13. Mon, D.-L.: Evaluating weapon system using fuzzy analytic hierarchy process based on entropy weight. In: Fuzzy Systems, International Joint conference of the Fourth IEEE International Conference on Fuzzy Systems and The Second International Fuzzy Engineering Symposium, Proceedings of 1995 IEEE International Conference on 20–24 (1995). ISBN: 0-7803-2461-7Google Scholar
  14. Podgorski, K. V., Kockelman, K. M.: Public perceptions of toll roads: a survey of the texas perspective. In: Transportation Research Board 86th Annual Meeting, CD-ROM. National Research Council, Washington, D.C. (2005)Google Scholar
  15. Saaty, T.L.: The Analytical Hierarchy Process. McGraw Hill, New York (1980)Google Scholar
  16. Saaty, T.L.: Axiomatic foundation of the analytic hierarchy process. Manag. Sci. 32(7), 841–855 (1986)CrossRefGoogle Scholar
  17. Saaty, T.L.: What is relative measurement? The ratio scale phantom. Math. Comput. Model. 17(4–5), 1–12 (1993)CrossRefGoogle Scholar
  18. Saaty, T.L., Vargas, L.G.: Models, Methods, Concepts & Applications of the Analytic Hierarchy Process. Kluwer Academic Publishers, Norwell, MA (2001)Google Scholar
  19. Simon, H.A.: Models of Thought. Yale Univ. Press, New Heaven, CT (1954)Google Scholar
  20. Vargas, L.G.: An overview of the analytic hierarchy process and its applications. Eur. J. Oper. Res. 48(1), 2–8 (1990)CrossRefGoogle Scholar
  21. Weber, E.H.: De Pulsu, Resorptione, Auditu et Tactu. Annotationes Anatomicae et Physiologicae. C. F. Köhler, Leipzig (1834)Google Scholar
  22. Yeh, C.-H., Deng, H.: An algorithm for fuzzy multi-criteria decision making. In: IEEE International Conference on Intelligent Processing Systems. Beijing, China (1997)Google Scholar
  23. Zadeh, L.: Fuzzy sets. Inform. Control 8, 338–353 (1965)CrossRefGoogle Scholar
  24. Zimmermann, H.J.: Fuzzy Set Theory—and its Applications. Kluwer Academic Publishers, Boston (1991)Google Scholar

Copyright information

© Springer Science+Business Media, LLC. 2008

Authors and Affiliations

  1. 1.Department of Civil EngineeringMustafa Kemal UniversityHatayTurkey

Personalised recommendations