International Journal of Fuzzy Systems

, Volume 19, Issue 5, pp 1512–1527 | Cite as

A Fuzzy Cognitive Map Approach Applied in Cost–Benefit Analysis for Highway Projects

  • Muhammed Emin Cihangir Bağdatlı
  • Rıfat Akbıyıklı
  • Elpiniki I. Papageorgiou
Article
  • 189 Downloads

Abstract

Cost–benefit analysis (CBA) is a method widely used all over the world for transport project appraisal. However, this method needs to handle the inherent uncertainty which affects the results negatively. In a highway project, there are high uncertainties due to a lack of data, future predictions, economic indeterminacy, etc. In conventional approaches, a risk analysis, which is based primarily on a sensitivity analysis and/or Monte Carlo simulation, is conducted in order to solve the problems mentioned above. However, these approaches present some main drawbacks. This study aims to investigate the usability and utility of a new approach in highways CBA in order to cope with uncertainty easily and in a more user-friendly way. To achieve the above-cited goal, the technique of a fuzzy cognitive map (FCM) was utilized due to its popularity in modeling complex problems. A decision-making FCM model including a RISK parameter was developed by experienced people/experts in this scientific domain to assess benefits and costs in highway projects. The developed FCM model focuses on minimizing the effects of uncertainty in the CBA for highways. Therefore, the concepts of conventional CBA were defined within the domain of risk analysis. The performance of the developed FCM model was tested through actual feasibility studies as well as through a specific case study. As a result of comparisons, promising results for validation of the developed FCM model are obtained.

Keywords

Cost–benefit analysis Decision making Fuzzy cognitive map Fuzzy risk analysis Highway projects Transport economic appraisal 

References

  1. 1.
    Akbıyıklı, R.: Engineering Economics Fundamental Principles and Applications. Birsen Press, Istanbul (2014). (in Turkish) Google Scholar
  2. 2.
    Akbıyıklı, R.: The Report of Economic Feasibility Study for Linking Highway Between NMM and D100, TEM, İzmit Bay Crossing, Kocaeli Metropolitan Municipality, Kocaeli, Turkey (2014)Google Scholar
  3. 3.
    Axelrod, R.: Structure of Decision: The Cognitive Maps of Political Elites. Princeton University Press, Princeton (1976)Google Scholar
  4. 4.
    Avineri, E., Prashker, J., Ceder, A.: Transportation projects selection process using fuzzy sets theory. Fuzzy Sets Syst. 116, 35–47 (2000)CrossRefGoogle Scholar
  5. 5.
    Bağdatlı, M.E.C., Akbıyıklı, R., Demir, A.: Utilisation of intelligent systems in the economical evaluation of transportation projects. Online J. Sci. Technol. 5(3), 78–84 (2015)Google Scholar
  6. 6.
    Bağdatlı, M.E.C., Akbıyıklı, R.: Ulaştırma yapıları ekonomik analizlerinde iskonto oranı: bir durum çalışması, Sakarya Üniversitesi Fen Bilimleri Enstitüsü Dergisi, Cilt 19, Sayı 1, Sayfa 67–74 (2015)Google Scholar
  7. 7.
    Campbell, H.F., Brown, R.P.C.: Benefit-Cost Analysis Financial and Economic Appraisal Using Spreadsheets. Cambridge University Press, New York (2003)CrossRefGoogle Scholar
  8. 8.
    Carr, V., Tah, J.H.M.: A fuzzy approach to construction project risk assessment and analysis: construction project risk management system. Adv. Eng. Softw. 32, 847–857 (2001)Google Scholar
  9. 9.
    Carvalho, J.P., Tomé, J.A.B.: Rule based fuzzy cognitive maps—a comparison with fuzzy cognitive maps. In: Proceedings of the NAFIPS99, New York, NY, USA (1999)Google Scholar
  10. 10.
    Carvalho, J.,P., Tome, J.A.B.: Rule Based Fuzzy Cognitive Maps—Qualitative Systems Dynamics. In: Proceedings of the 19th International Conference of the North American Fuzzy Information Processing Society, NAFIPS2000, Atlanta (2000)Google Scholar
  11. 11.
    Damart, S., Roy, B.: The uses of cost–benefit analysis in public transportation decision-making in France. Transp. Policy 16(2009), 200–212 (2009)CrossRefGoogle Scholar
  12. 12.
    EC: Guide to Cost Benefit Analysis of Investment Projects. European Commission Directorate General Regional Policy (2008)Google Scholar
  13. 13.
    Feng, C., Wang, S.: Integrated cost–benefit analysis with environmental factors for a transportation project: case of Pinglin interchange in Taiwan. J. Urban Plan. Dev. 133(3), 172–178 (2007)Google Scholar
  14. 14.
    FHWA: Economic Analysis Primer. Federal Highway Administration. Office of Asset Management, U.S. Department of Transportation, Washington, DC (2003)Google Scholar
  15. 15.
    Godinho, P. Dias, J.: Cost–benefit analysis and the optimal timing of road infrastructures. J. Infrastruct. Syst. 18(4), 261–269 (2012)Google Scholar
  16. 16.
    Groumpos, P.P.: Fuzzy cognitive maps: basic theories and their application to complex systems. In: Glykas, M. (ed.) Fuzzy Cognitive Maps Studies in Fuzziness and Soft Computing, vol. 247. Springer, Berlin (2010)Google Scholar
  17. 17.
    Haezendonck, E.: Transport Project Evaluation Extending the Social Cost–Benefit Approach. Edward Elgar Publishing Ltd, Cheltenham (2007)Google Scholar
  18. 18.
    Hurtado, S.M.: Modeling of operative risk using fuzzy expert systems. In: Glykas, M. (ed.) Fuzzy Cognitive Maps. Springer, Berlin (2010)Google Scholar
  19. 19.
    Jones, H., Moura, F., Domingos, T.: Transport infrastructure project evaluation using cost–benefit analysis. Procedia Soc. Behav. Sci. 111, 400–409 (2014)CrossRefGoogle Scholar
  20. 20.
    Kosko, B.: Fuzzy cognitive maps. Int. J. Man Mach. Stud. 24, 65–75 (1986)CrossRefMATHGoogle Scholar
  21. 21.
    Lazzerini, B., Mkrtchyan, L.: Analyzing risk impact factors using extended fuzzy cognitive maps. IEEE Syst. J. 5(2), 288–297 (2011)Google Scholar
  22. 22.
    Lazzerini, B., Mkrtchyan, L.: Pessimistic evaluation of risks using ranking of generalized fuzzy numbers. In: IEEE Systems Conference (2010)Google Scholar
  23. 23.
    Maravas, A., Pantouvakis, J.P., Lambropoulos, S.: Modeling uncertainty during cost benefit analysis of transportation projects with the aid of fuzzy set theory. Procedia Soc. Behav. Sci. 48, 3661–3670 (2012)CrossRefGoogle Scholar
  24. 24.
    MN/DOT: Benefit Cost Analysis Guidance. Minnesota Department of Transportation, St. Paul (2005)Google Scholar
  25. 25.
    Mouter, N., Annema, J.A., Wee, B.V.: Attitudes towards the role of cost benefit analysis in the decision-making process for spatial-infrastructure projects: a Dutch case study. Transp. Res. Part A 58, 1–14 (2013)Google Scholar
  26. 26.
    Özkir, V., Demirel, T.: A fuzzy assessment framework to select among transportation investment projects in Turkey. Expert Syst. Appl. 39, 74–80 (2012)Google Scholar
  27. 27.
    Papageorgiou, E.I., Markinos, A.T., Gemtos, T.A.: Soft computing technique of fuzzy cognitive maps to connect yield defining parameters with yield in cotton crop production in central Greece as a basis for a decision support system for precision agriculture application. In: Glykas, M. (ed.) Fuzzy Cognitive Maps Studies in Fuzziness and Soft Computing, vol. 247. Springer, Berlin (2010)Google Scholar
  28. 28.
    Papageorgiou, E.I., Spyridonos, P.P., Glotsos, D.T., Stylios, C.D., Ravazoula, P., Nikiforidis, G.N., Groumpos, P.P.: Brain tumour characterization using the soft computing technique of fuzzy cognitive maps. Appl. Soft Comput. 8, 820–828 (2008)CrossRefGoogle Scholar
  29. 29.
    Papageorgiou, E.I. (ed.) Fuzzy Cognitive Maps for Applied Sciences and Engineering—From Fundamentals to Extensions and Learning Algorithms. Intelligent Systems Reference Library No. 54, Springer, Berlin, ISBN: 978-3-642-39738-7 (2014)Google Scholar
  30. 30.
    Papageorgiou, E.I., Salmeron, J.L.: A review of fuzzy cognitive map research at the last decade. IEEE Trans. Fuzzy Syst. (IEEE TFS) 21(1), 66–79 (2013)CrossRefGoogle Scholar
  31. 31.
    Papageorgiou, E.I.: A new methodology for decisions in medical informatics using fuzzy cognitive maps based on fuzzy rule-extraction techniques. Appl. Soft Comput. 11, 500–513 (2011)CrossRefGoogle Scholar
  32. 32.
    Papageorgiou, E.I., Markinos, A., Gemtos, T.: Application of fuzzy cognitive maps for cotton yield management in precision farming. Expert Syst. Appl. 36(10), 12399–12413 (2009)CrossRefGoogle Scholar
  33. 33.
    Papageorgiou, E.I.: Review study on fuzzy cognitive maps and their applications during the last decade. In: Glykas, M. (ed.) Business Process Management, SCI, vol. 444, pp. 281–298. Springer, Berlin (2013)Google Scholar
  34. 34.
    Rezakhani, P.: Fuzzy risk analysis model for construction projects. Int. J. Civ. Struct. Eng. 2(2), 507–522 (2011)Google Scholar
  35. 35.
    Salling, K.B., Banister, D.: Assessment of large transport infrastructure projects: the CBA-DK model. Transp. Res. Part A 43, 800–813 (2009)Google Scholar
  36. 36.
    Salling, K.B., Leleur, S.: Modelling of transport project uncertainties: risk assessment and scenario analysis. Eur. J. Transp. Infrastruct. Res. 12(1), 21–38 (2012)Google Scholar
  37. 37.
    Salling, K.B., Leleur, S.: Transport appraisal and Monte Carlo simulation by use of the CBA-DK model. Transp. Policy 18, 236–245 (2011)CrossRefGoogle Scholar
  38. 38.
    Shakhsi-Niaei, M., Torabi, S.A., Iranmanesh, S.H.: A comprehensive framework for project selection problem under uncertainty and real-world constraints. Comput. Ind. Eng. 61, 226–237 (2011)CrossRefGoogle Scholar
  39. 39.
    Stylios, C.D., Groumpos, P.P.: Modeling complex systems using fuzzy cognitive maps. IEEE Trans. Syst. Man Cybern Part A Syst. Hum. 34(1), 155–162 (2004)Google Scholar
  40. 40.
    TCK: Highway Economy and Project Evaluation Techniques, Course Book. General Directorate of Turkish Highways, Ankara (2013) (in Turkish) Google Scholar
  41. 41.
    Teng, J.Y., Tzeng, G.H.: Transportation investment project selection using fuzzy multiobjective programming. Fuzzy Sets Syst. 96(3), 259–280 (1998)CrossRefGoogle Scholar
  42. 42.
    Yaman, D., Polat, S.: A fuzzy cognitive map approach for effect-based operations: an illustrative case. J. Inf. Sci. 179(4), 382–403 (2009)CrossRefGoogle Scholar
  43. 43.
    Yayla, N.: Highway Engineering. Birsen Press, Istanbul (2008). (in Turkish) Google Scholar
  44. 44.
    Zhao, T., Sundararajan, S.K., Tseng, C.: Highway development decision-making under uncertainty: a real options approach. J. Infrastruct. Syst. 10(1), 23–32 (2004)Google Scholar

Copyright information

© Taiwan Fuzzy Systems Association and Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • Muhammed Emin Cihangir Bağdatlı
    • 1
  • Rıfat Akbıyıklı
    • 2
  • Elpiniki I. Papageorgiou
    • 3
    • 4
  1. 1.Department of Civil Engineering, Engineering FacultySakarya UniversitySakaryaTurkey
  2. 2.Department of Civil Engineering, Technology FacultyDüzce UniversityDüzceTurkey
  3. 3.Faculty of Business EconomicsHasselt UniversityHasseltBelgium
  4. 4.Technological Educational Institute (T.E.I.) of Central GreeceLamiaGreece

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