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.
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Appendix: Identification and Description of Risks in CBA for Highway Projects
Appendix: Identification and Description of Risks in CBA for Highway Projects
Risk likelihood | Risk severity |
|---|---|
AC | |
1—Including missing data in the accident reports □ Very unlikely □ Unlikely □ Medium □ Likely □ Very likely | 1—The effect of this risk on the CBA □ Very low □ Low □ Medium □ High □ Very high |
2—Including missing data in the accident statistics □ Very unlikely □ Unlikely □ Medium □ Likely □ Very likely | 2—The effect of this risk on the CBA □ Very low □ Low □ Medium □ High □ Very high |
3—Including wrong data in the accident statistics □ Very unlikely □ Unlikely □ Medium □ Likely □ Very likely | 3—The effect of this risk on the CBA □ Very low □ Low □ Medium □ High □ Very high |
4—Wrong determination of accident unit prices □ Very unlikely □ Unlikely □ Medium □ Likely □ Very likely | 4—The effect of this risk on the CBA □ Very low □ Low □ Medium □ High □ Very high |
5—Changing of discount rate □ Very unlikely □ Unlikely □ Medium □ Likely □ Very likely | 5—The effect of this risk on the CBA □ Very low □ Low □ Medium □ High □ Very high |
TV | |
1—Wrong determination of unit prices of the travel time □ Very unlikely □ Unlikely □ Medium □ Likely □ Very likely | 1—The effect of this risk on the CBA □ Very low □ Low □ Medium □ High □ Very high |
2—Wrong calculation of gaining time □ Very unlikely □ Unlikely □ Medium □ Likely □ Very likely | 2—The effect of this risk on the CBA □ Very low □ Low □ Medium □ High □ Very high |
3—Wrong determination of time value of the load □ Very unlikely □ Unlikely □ Medium □ Likely □ Very likely | 3—The effect of this risk on the CBA □ Very low □ Low □ Medium □ High □ Very high |
4—Wrong calculation of the existing traffic □ Very unlikely □ Unlikely □ Medium □ Likely □ Very likely | 4—The effect of this risk on the CBA □ Very low □ Low □ Medium □ High □ Very high |
5—Wrong estimation of the future traffic □ Very unlikely □ Unlikely □ Medium □ Likely □ Very likely | 5—The effect of this risk on the CBA □ Very low □ Low □ Medium □ High □ Very high |
6—Changing of discount rate □ Very unlikely □ Unlikely □ Medium □ Likely □ Very likely | 6—The effect of this risk on the CBA □ Very low □ Low □ Medium □ High □ Very high |
VOC | |
1—Wrong determination of unit prices of the VOC □ Very unlikely □ Unlikely □ Medium □ Likely □ Very likely | 1—The effect of this risk on the CBA □ Very low □ Low □ Medium □ High □ Very high |
2—Wrong calculation of the existing traffic □ Very unlikely □ Unlikely □ Medium □ Likely □ Very likely | 2—The effect of this risk on the CBA □ Very low □ Low □ Medium □ High □ Very high |
3—Wrong estimation of the future traffic □ Very unlikely □ Unlikely □ Medium □ Likely □ Very likely | 3—The effect of this risk on the CBA □ Very low □ Low □ Medium □ High □ Very high |
4—Changing of discount rate □ Very unlikely □ Unlikely □ Medium □ Likely □ Very likely | 4—The effect of this risk on the CBA □ Very low □ Low □ Medium □ High □ Very high |
OMC | |
1—Wrong estimation of the OMC □ Very unlikely □ Unlikely □ Medium □ Likely □ Very likely | 1—The effect of this risk on the CBA □ Very low □ Low □ Medium □ High □ Very high |
2—Changing of discount rate □ Very unlikely □ Unlikely □ Medium □ Likely □ Very likely | 2—The effect of this risk on the CBA □ Very low □ Low □ Medium □ High □ Very high |
CC | |
1—Changing of unit prices of the CC □ Very unlikely □ Unlikely □ Medium □ Likely □ Very likely | 1—The effect of this risk on the CBA □ Very low □ Low □ Medium □ High □ Very high |
2—Changing of discount rate □ Very unlikely □ Unlikely □ Medium □ Likely □ Very likely | 2—The effect of this risk on the CBA □ Very low □ Low □ Medium □ High □ Very high |
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Bağdatlı, M.E.C., Akbıyıklı, R. & Papageorgiou, E.I. A Fuzzy Cognitive Map Approach Applied in Cost–Benefit Analysis for Highway Projects. Int. J. Fuzzy Syst. 19, 1512–1527 (2017). https://doi.org/10.1007/s40815-016-0252-3
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DOI: https://doi.org/10.1007/s40815-016-0252-3