Prioritizing factors affecting traffic volume of public-private partnership infrastructure projects

  • Authors

    • Phong Thanh Nguyen
    • Veerasak Likhitruangsilp
    • Masamitsu Onishi
    https://doi.org/10.14419/ijet.v7i4.21526

  • Public-private partnership (PPP) is an effective alternative for raising capital for infrastructure projects and has been a popular trend in de-veloping countries recently. A key factor that affects the success of PPP transportation projects is traffic demand because it directly influ-ences project revenue. Inaccurate traffic demand estimates may lead to financial difficulties for private partners. This paper applies fuzzy extended analytic method (FEAM) to prioritize critical factors that affect traffic volume of PPP infrastructure projects. The results benefit both public and private sectors for realizing key factors for the success of PPP infrastructure project implementation.

  • References

    1. [1] Alasad, R., et al., Prioritization of Demand Risk Factors in PPP Infrastructure Projects, Construction Research Congress 2014: Construction in a Global Network, Castro-Lacouture, D., et al., eds., ASCE, 1359-1368, 2014.

      [2] Delmon, J., Project finance, BOT projects and risk, ed. 1, Kluwer Law International, 2005.

      [3] Yu, C.Y. & Lam. K.C., A Decision Support System for the determination of concession period length in transportation project under BOT contract, Automation in Construction, 31, pp. 114-127, May. 2013. https://doi.org/10.1016/j.autcon.2012.11.012

      [4] Thomas, A.V., Kalidindi, S.N. & Ganesh, L.S., Modelling and assessment of critical risks in BOT road projects, Construction Management and Economics, 24(4), pp.407-424, April. 2006.

      https://doi.org/10.1080/01446190500435275

      [5] Thomas, A.V., Kalidindi. S.N., & Ananthanarayanan, K, Risk perception analysis of BOT road project participants in India, Construction Management and Economics. 21(4), pp. 393-407, June. 2003. https://doi.org/10.1080/0144619032000064127

      [6] Osei-Kyei, R. & Chan, A.P.C, Review of studies on the Critical Success Factors for Public–Private Partnership (PPP) projects from 1990 to 2013, International Journal of Project Management, 33(6), pp.1335-1346, 2015.

      https://doi.org/10.1016/j.ijproman.2015.02.008.

      [7] Yu, C., Lam, K., & Yung. P., Factors That Influence the Concession Period Length for Tunnel Projects under BOT Contracts, Journal of Management in Engineering, 30(1), pp.108-121, Jan. 2014.

      https://doi.org/10.1061/(ASCE)ME.1943-5479.0000180

      [8] Alasad, R. & Motawa, I., Dynamic demand risk assessment for toll road projects. Construction Management and Economics, 33(10), pp.799-817, Feb. 2016.

      https://doi.org/10.1080/01446193.2016.1143561

      [9] Walker, C.T. & Smith, A.J., Privatized infrastructure: The build operate transfer approach, ed. 1, Thomas Telford, 1995. https://doi.org/10.1680/pitba.20535.

      [10] Shen, L.Y. & Wu, Y.Z., Risk concession model for build/operate/transfer contract projects, Journal of Construction Engineering and Management, 131(2), pp.211-220, Feb. 2005.

      https://doi.org/10.1061/(ASCE)0733-9364(2005)131:2(211)

      [11] Babatunde, S.O. & Perera, S., Analysis of traffic revenue risk factors in BOT road projects in developing countries, Transport Policy, 56, pp.41-49, Mar. 2017.https://doi.org/10.1016/j.tranpol.2017.03.012

      [12] Zhou, J., Chen, X.G. & Yang. H.W., Control Strategy on Road Toll Pricing under a BOT Scheme, Systems Engineering - Theory & Practice, 28(2), pp.148-151, Feb. 2008.

      https://doi.org/10.1016/S1874-8651(09)60014-4

      [13] Chiu, T. & Bosher, C., Risk Sharing in Various Public Private Partnership (PPP) Arrangements for the Provision of Water and Wastewater Services, Conference on Public Private Partnerships–Opportunities and Challenges. pp.01-11, 2005.

      [14] Lang, L.H.P., Project finance in Asia, Advances in finance, investment, and banking, Vol. 6. Amsterdam, ed. 1, New York, Elsevier, 1998.

      [15] Nasution, MDTP, et al., Decision Support Rating System with Analytical Hierarchy Process Method, International Journal of Engineering & Technology, 7 (2.3), pp.105-108.

      https://doi.org/10.14419/ijet.v7i2.3.12629

      [16] Sona1, P., et al., Design of a multi criteria decision model-fuzzy analytical hierarchy approach, International Journal of Engineering & Technology, 7 (1.1), pp.116-120, 2018.

      https://doi.org/10.14419/ijet.v7i1.1.9209

      [17] Nguyen, T.P, et al., Application of Fuzzy Analytic Network Process and TOPSIS Method for Material Supplier Selection, Key Engineering Materials, 728, pp. 411-415, 2017.

      https://doi.org/10.4028/www.scientific.net/KEM.728.411.

      [18] Mateo, J.R.S.C., Multi Criteria Analysis in the Renewable Energy Industry, ed. 1, Springer, 2012.

      https://doi.org/10.1007/978-1-4471-2346-0

      [19] Thipparat, T., Chovichien, V., & Lorterapong, P., A fuzzy multiple criteria decision framework for engineering performance evaluation, International Journal of Technology Intelligence and Planning, 5(3), pp. 322-340, Sep. 2009.

      https://doi.org/10.1504/IJTIP.2009.026752

      [20] Moghadam, M.K., Jahromi, A.R.M. & Nooramin, A.S., A fuzzy AHP decision support system for selecting yard cranes in marine container terminals, WMU Journal of Maritime Affairs, 10(2), pp.227-240, July. 2011.

      https://doi.org/10.1007/s13437-011-0007-9

      [21] ErtuÄŸrul, Ä°. & KarakaÅŸoÄŸlu, N., Comparison of fuzzy AHP and fuzzy TOPSIS methods for facility location selection, The International Journal of Advanced Manufacturing Technology, 39(7), pp.783-795, 2008. https://doi.org/10.1007/s00170-007-1249-8

      [22] Duru, O., et al., Multi-layer quality function deployment (QFD) approach for improving the compromised quality satisfaction under the agency problem: A 3D QFD design for the asset selection problem in the shipping industry, Quality & Quantity, pp.01-22, June. 2013. https://doi.org/10.1007/s11135-011-9653-4

      [23] Cebeci, U. & Ruan, D., A multi-attribute comparison of Turkish quality consultants by fuzzy AHP. International Journal of Information Technology & Decision Making, 6(01), pp.191-207, Jan. 2007. https://doi.org/10.1142/S0219622007002423

      [24] Do, T.S., et al., Different perceptions of concern factors for strategic investment of the private sector in public - private partnership transportation projects, ASEAN Engineering Journal, 5(2), pp. 05-25. Dec. 2016.

      [25] Nguyen TP, et al., “Developing a stochastic traffic volume prediction model for public-private partnership projectsâ€, AIP Conference Proceedings, 1903, 060010 (2017). https://doi.org/10.1063/1.5011564.

      [26] Nguyen TP & Likhitruangsilp V, Risk Factors Affecting Concession Period Length For Public Infrastructure Projects, International Journal of Civil Engineering and Technology, 8(6), pp. 345–348, 2017.

      Do, T.S., et al., Impacts o.f risk factors on the performance of Public-Private Partnership transportation projects in Vietnam, ASEAN Engineering Journal, 6(1), pp. 1-24, Mar. 2017

  • Downloads

  • How to Cite

    Nguyen, P. T., Likhitruangsilp, V., & Onishi, M. (2018). Prioritizing factors affecting traffic volume of public-private partnership infrastructure projects. International Journal of Engineering & Technology, 7(4), 2988-2991. https://doi.org/10.14419/ijet.v7i4.21526