KSCE Journal of Civil Engineering

, Volume 17, Issue 5, pp 1109–1116 | Cite as

A fuzzy AHP model for assessing the condition of metro stations

  • Konstantinos KepaptsoglouEmail author
  • Matthew G. Karlaftis
  • Jason Gkountis


Service quality, performance, and attractiveness of transit systems are related to the condition of their infrastructures; it is therefore desirable for transit infrastructures to be in the best possible condition and offer an attractive, safe, and friendly environment for travelers. This paper presents a model for rating the condition and performance of transit infrastructures and particularly metro stations; the model is derived based on the opinion of a group of experts (group decision making) and explicitly considers uncertainty. Data from the Athens Metro system are used and a Fuzzy Analytical Hierarchy Process technique is exploited in an effort to capture inherent uncertainties and ambiguities found in expert opinions. Results were compared to those of regular AHP and it was found that based upon the degree of uncertainty, differences in resulting weights could be up to 8%.


metro stations condition rating fuzzy AHP uncertainty 


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  1. Abu-Mallouh, M. (1999). Model for Station Rehabilitation and Planning (MSRP), PhD Thesis, Construction Eng. and Mgmt. Department, Civil Eng, Polytechnic University, NY, USA.Google Scholar
  2. Ahn, B. S. (2000). “The analytic hierarchy process in an uncertain environment: A simulation approach by hauser and tadikamalla (1996).” Eur. J. Oper. Res., Elsevier, Vol. 124, No. 1, pp. 217–218.zbMATHCrossRefGoogle Scholar
  3. Ahn, B. S., Park, K. S., Han, C. H., and Kim, J. K. (2000). “Multiattribute decision aid under incomplete information and hierarchical structure.” Eur. J. Oper. Res., Elsevier, Vol. 125, No. 2, pp. 431–439.zbMATHCrossRefGoogle Scholar
  4. Alias, M. A., Hashim, S. Z. M., and Samsudin, S. (2009). “Using fuzzy analytic hierarchy process for southern johor river ranking.” Int. J. Soft. Comp. App., Vol. 1, No. 1, pp. 62–76.Google Scholar
  5. Anderson, M. D. and Sadlin, A. B. (2001). “A rural transit vehicle management system and condition predictor model.” J. Public. Trans., NCTR, Vol. 4, No. 1, pp. 59–71.Google Scholar
  6. Belton, V. and Stewart, T. J. (2002). Multiple criteria decision analysis, Springer, Berlin, Germany.CrossRefGoogle Scholar
  7. Bertini, R. L. and El-Geneidy, A. (2003). “Using archived data to generate transit performance measures.” Proc. 82nd Transportation Research Board Annual Meeting, Washington D.C., USA.Google Scholar
  8. Bouyssou, D., Marchant, T., Pirlot, M., Perny, P., Tsoukias, A., and Vincke, P. (2000). Evaluation models: A critical perspective, Kluwer, Boston, USA.zbMATHGoogle Scholar
  9. Buckley, J. J. (1985). “Fuzzy hierarchical analysis.” Fuzzy Set Sys, Elsevier, Vol. 17, No. 3, pp. 233–247.MathSciNetzbMATHCrossRefGoogle Scholar
  10. Buckley, J. J., Feuring, T., and Hayashii. Y. (2001). “Fuzzy hierarchical analysis revisited.” Eur. J. Oper. Res., Elsevier, Vol. 129, No. 1, pp. 48–64.zbMATHCrossRefGoogle Scholar
  11. Chang, D. Y. (1996). “Applications of the extent analysis method on AHP.” Eur. J. Op. Res., Elsevier, Vol. 95, No. 3, pp. 649–655.zbMATHCrossRefGoogle Scholar
  12. Chang, C. W., Wu, C. R., Lin, C. T., and Chen, H. C. (2008a). “Evaluating and controlling silicon wafer slicing quality using fuzzy analytical hierarchy and sensitivity analysis.” Int. J. Adv. Manuf. Tech., Elsevier, Vol. 36, No.1, pp. 322–333.CrossRefGoogle Scholar
  13. Chang, C. W., Wu, C. R., and Chen, H. C. (2008b). “Using expert technology to select unstable slicing machine to control wafer slicing quality via fuzzy AHP.” Expert. Syst. Appl., Elsevier, Vol. 34, No. 3, pp. 2210–2220.MathSciNetCrossRefGoogle Scholar
  14. Dyer, J. (2005). “MAUT — Multiattribute utility theory.” Multiple Criteria Decision Analysis: State of the Art Surveys (International Series in Operations Research and Management Science), Figueira, J., Greco, S. and Ehrogott, M., eds., 78, Springer, NY, pp. 265–292.Google Scholar
  15. Eskandari, H. and Rabelo, L. (2007). “Handling uncertainty in the analytic hierarchy process: A stochastic approach.” Int. J. Tech. Decis., World Scientific Publishing, Vol. 6, No. 1, pp. 177–189.zbMATHCrossRefGoogle Scholar
  16. Hastak, M. and Abu-Mallouh, M. M. (2001). “MSRP: Model for Station Rehabilitation Planning.” J. Infrastruct. Sys., ASCE, Vol. 7, No. 2, pp. 58–66.CrossRefGoogle Scholar
  17. Karlaftis, M. G. and Sinha, K. C. (1997). “Modeling approach for transit rolling-stock deterioration prediction.” J. Transp. Eng., ASCE, Vol. 123, No. 3, pp. 223–228.CrossRefGoogle Scholar
  18. Leung, L. C. and Chao, D. (2000). “On consistency and ranking of alternatives in fuzzy AHP.” Eur. J. Oper. Res., Elsevier, Vol. 124, No. 2, pp. 102–113.zbMATHCrossRefGoogle Scholar
  19. Lipshitz, R. and Strauss, O. (1997). “Coping with uncertainty: A naturalistic decision-making analysis.” Organ. Beh. Human Dec., Elsevier, Vol. 69, No.1, pp. 149–163.CrossRefGoogle Scholar
  20. Mazzulla, G. and Eboli, L. (2006). “A service quality experimental measure for public transport.” Eur. Trans., ISTIEE, Vol. 34, No.1, pp. 42–53.Google Scholar
  21. Meixner, O. (2009). “Fuzzy AHP group decision analysis and its application for the evaluation of energy sources.” Proc 10th International Symposium on the Analytic Hierarchy/Network Process Multicriteria Decision Making, University of Pittsburgh Pittsburgh, Pennsylvania, USA.Google Scholar
  22. Miranoni, O. (2006). “A discussion on the computational limitations of outranking methods for land use suitability assessment.” Int. J. Geog. Inf. Sci., Taylor & Francis, Vol. 20, No. 1, pp. 69–87.CrossRefGoogle Scholar
  23. QUATTRO (1998). QUATTRO final report, Final Deliverable,, EU.Google Scholar
  24. Roy, B., Present, M., and Silhol, D. (1986). “A programming method for determining which paris metro stations should be renovated.” Eur. J. Oper. Res., Vol. 24, No. 2, pp. 318–334.CrossRefGoogle Scholar
  25. Saaty, T. L. (1980). The analytic hierarchy process, McGraw Hill, NY.zbMATHGoogle Scholar
  26. Semaan, N. (2006). Subway Station Diagnosis Index (SSDI): A condition assessment model. MSc Thesis, Department of Building, Civil and Environmental Engineering, Concordia University, Canada.Google Scholar
  27. Semaan, N. and Zayed, T. (2009). “Subway station diagnosis index condition assessment model.” J. Infrastruct. Sys., ASCE, Vol. 15, No. 3, pp. 222–231.CrossRefGoogle Scholar
  28. Semaan, N. and Zayed, T. (2010). “A stochastic diagnostic model for subway stations.” Tunn. Undergr Sp. Tech., Elsevier, Vol. 25, No. 1, pp. 32–41.CrossRefGoogle Scholar
  29. Tag, Y.-C. and Beynon, M. J. (2005). “Application and development of a fuzzy analytic hierarchy process within a capital investment study.” J. Econ. Manag., Elsevier, Vol. 1, No. 2, pp. 207–230.Google Scholar
  30. TRB (1999). A Handbook for measuring customer satisfaction and service quality, TCRP Report 47 (, Washington D.C., USA.Google Scholar
  31. Van Laarhoven, P. J. M. and Pedrycz, W. (1983). “A fuzzy extension of Saaty’s priority theory.” Fuzzy Set. Syst., Elsevier, Vol. 11, No. 3, pp. 229–241.zbMATHGoogle Scholar
  32. Wang, Y-M., Luo, Y. and Hua, Z. (2008). “On the extent analysis method for fuzzy AHP and its applications.” Eur. Jour. Oper. Res., Elsevier, Vol. 186, No. 2, pp. 735–747.zbMATHCrossRefGoogle Scholar
  33. Winston, W. (2003). Operations research: Applications and algorithms. Duxbury Press, NY, USA.Google Scholar
  34. Zimmerman, H-J. (2001). Fuzzy set theory and its applications, 4th ed., Kluwer, Massachusetts, USA.CrossRefGoogle Scholar

Copyright information

© Korean Society of Civil Engineers and Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Konstantinos Kepaptsoglou
    • 1
    Email author
  • Matthew G. Karlaftis
    • 1
  • Jason Gkountis
    • 1
  1. 1.Dept. of Transportation Planning and Engineering, School of Civil EngineeringNational Technical University of AthensZografouGreece

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