Uncertainties in Transportation Infrastructure Development and Management

Conference paper

Abstract

The development and management of transportation infrastructure is a continuous process that includes the phases of planning and design, construction, operations, maintenance, preservation, and reconstruction. Uncertainties at each phase include variability in demand estimation, reliability of planning and design parameters, construction cost overruns and time delay, unexpected outcomes of operational policies and maintenance and preservation strategies, and risks of unintended disruption due to incidents or sudden extreme events. These variabilities, which are due to inexact levels of natural and anthropogenic factors in the system environment, are manifest ultimately in the form of variable outcomes of specific performance measures established for that phase. Transportation infrastructure managers seek to adequately identify and describe these uncertainties through a quantitative assessment of the likelihood and consequence of each of possible level of the performance outcome and to incorporate these uncertainties into the decision-making process. This chapter identifies major sources of uncertainties at different phases of transportation infrastructure development and management and examines the methods of their measurements. Finally, this chapter presents several approaches to incorporate uncertainties in transportation infrastructure decision-making and provides future directions for research.

Keywords

Transportation infrastructure Uncertainty Extreme events Decision-making 

References

  1. 1.
    Bell MGH, Schmoecker JD, Iida Y, Lam WHK (2002) Transit network reliability: an application of absorbing Markov chains. Transportation and traffic theory in the 21st century. In: Proceedings of the 15th international symposium on transportation and traffic theory. University of South Australia, Adelaide, Australia. Accessed 10 Oct 2011Google Scholar
  2. 2.
    Bhargava A, Labi S, Sinha KC (2010) Development of a framework for Ex Post Facto evaluation of highway project costs in Indiana. Publication# FHWA/IN/JTRP-2009/33. Joint Transportation Research Program, Indiana Department of Transportation and Purdue University, West Lafayette, INGoogle Scholar
  3. 3.
    Blogspot.com (2007) Light rail will save us. http://victoriataftkpam.blogspot.com/2007/12/light-rail-will-save-us.html. Accessed October 10 2011
  4. 4.
    Bordat C, McCullouch BG, Sinha KC, Labi S (2004) An Analysis of Cost Overruns and Time Delays of INDOT Projects. Joint Transportation Research Program, Indiana Department of Transportation and Purdue University, West Lafayette, IndianaGoogle Scholar
  5. 5.
    Butt AA, Shahin MY, Feighan KJ, Carpenter SH (1987) Pavement performance prediction model using the Markov process. Transportation research record no. 1123, pp 12–19Google Scholar
  6. 6.
    Buzzfeed.com (2011) Road split in two by Japanese earthquake. http://www.buzzfeed.com/burnred/road-split-in-two-by-japanese-earthquake-281t. Accessed 10 Oct 2011
  7. 7.
    Cbslocal.com (2011) Third mudslide closes Highway 1 on Big Sur coast. http://sanfrancisco.cbslocal.com/2011/03/28/third-mudslide-closes-highway-1-on-big-sur-coast/. Accessed 10 Oct 2011
  8. 8.
    Clemen RT (1996) Making hard decisions. Duxbury Press, Pacific GroveGoogle Scholar
  9. 9.
    Cope A, Bai Q, Samdariya A, Labi S (2011) Assessing the efficacy of stainless steel for bridge deck reinforcement under uncertainty using Monte Carlo simulation. Struct Infrastruct Eng. doi:10.1080/15732479.2011.602418
  10. 10.
    ENR (2001) Bay bridge replacement comes in above estimate. ENR, p. 5, Dec. 31, 2001Google Scholar
  11. 11.
    ENR (2002) Virginia’s big ‘mixing bowl’ is 180% over budget. ENR, p. 7, Dec. 2, 2002Google Scholar
  12. 12.
    Ersahin T, McCabe B, Doyle M (2003) Monte Carlo simulation analysis at Lester B Pearson International Airport development project. Construction Research Congress. Winds of change: integration and innovation in construction. Proceedings of the Congress, Honolulu, Hawaii, United StatesGoogle Scholar
  13. 13.
    Flyvbjerg B, Holm MKS, Buhl SL (2006) Inaccuracy in traffic forecasts. Transp Rev 26(1):1–24CrossRefGoogle Scholar
  14. 14.
    Ford K, Arman M, Labi S, Sinha KC, Shirole A, Thompson P, Li Z (2011) Methodology for estimating life expectancies of highway assets (draft). School of Civil Engineering, Purdue University, West LafayetteGoogle Scholar
  15. 15.
    Fu G, Devaraj D (2008) Methodology of Homogeneous and Non-Homogeneous Markov Chains for Modeling Bridge Element Deterioration. Wayne State University, Detroit, MIGoogle Scholar
  16. 16.
    Jiang Y, Sinha KC (1989) Bridge service life prediction model using the Markov chain. Transportation research record no. 1223, pp 24–30Google Scholar
  17. 17.
    Kanafani A (1981) Transportation Demand Analysis. John Wiley and Sons, New York, NYGoogle Scholar
  18. 18.
    Keeney RL, Raiffa H (1993) Decisions with multiple objectives: Preferences and value tradeoffs. Cambridge University Press, New YorkGoogle Scholar
  19. 19.
    Li Z, Sinha KC (2004) Methodology for the development of a highway asset management system for Indiana. Purdue University, West LafayetteCrossRefGoogle Scholar
  20. 20.
    Li N, Xie WC, Haas R (1996) Reliability-based processing of Markov chains for modeling pavement network deterioration. Transportation research record no. 1524, pp 203–213Google Scholar
  21. 21.
    Majid MZA, McCaffer R (1998) Factors of Non-Excusable Delays that Influence Contractors’ Performance. J Manag Eng 14(3):42–48Google Scholar
  22. 22.
    Nagai K, Tomita Y, Fujimoto Y (1985) A fatigue crack initiation model and the life estimation under random loading by Monte Carlo method. J Soc Naval Archit Jpn 158(60):552–564Google Scholar
  23. 23.
    Nesbitt DM, Sparks GA, Neudorf RD (1993) A semi-Markov formulation of the pavement maintenance optimization problem. Can J Civil Eng XX(III):436–447CrossRefGoogle Scholar
  24. 24.
    NYSDOT (2002) Vulnerability manuals. Bridge Safety Program, New York State DOTGoogle Scholar
  25. 25.
    Oxford University Press (2011) Uncertainty. In: Oxford English Dictionary Online. http://dictionary.oed.com. Accessed October 10 2011
  26. 26.
    Pickrell DH (1990) Urban rail transit projects: forecast versus actual ridership and cost. US Department of Transportation, Washington, DCGoogle Scholar
  27. 27.
    Richmond JED (1998) New rail transit investments: a review. John F. Kennedy School of Government, Harvard University, Cambridge, MAGoogle Scholar
  28. 28.
    Riddell WT, Lynch J (2005) A Markov chain model for fatigue crack growth, inspection and repair: the relationship between probability of detection, reliability and number of repairs in fleets of railroad tank cars. In: Proceedings of the ASME pressure vessels and piping conference 2005 – operations, applications and components, Denver, Colorado, United StatesGoogle Scholar
  29. 29.
    Sarewitz D, Pielke RA (2001) Extreme events: a research and policy framework for disasters in context, Int Geol Rev, 43(5):406–418Google Scholar
  30. 30.
    Shackle GLS (1949) Expectation in economics, 2nd edn. Cambridge University Press, CambridgeGoogle Scholar
  31. 31.
    Shoukry SN, Martinelli DR, Reigle JA (1997) Universal pavement distress evaluator based on fuzzy sets. Transportation research record, no. 1592, pp 180–186Google Scholar
  32. 32.
    Sinha KC, Labi S, McCullouch B, Bhargava A, Bai Q (2009) Updating and enhancing the Indiana Bridge Management System (IBMS). Joint Transportation Research Program, Purdue University, West LafayetteCrossRefGoogle Scholar
  33. 33.
    Vidalis SM, Najafi FT (2002) Cost and time overruns in highway construction. In: 4th transportation specialty conference of the Canadian Society for Civil Engineering, Montréal, QC, Canada, 5–8 June 2002Google Scholar
  34. 34.
    Walls J, Smith MR (1998) Life-cycle cost analysis in pavement design – interim technical bulletin. Federal Highway Administration, SW Washington, DCGoogle Scholar
  35. 35.
    Workman SL (2008) Predicted vs. actual costs and ridership of new starts projects. In: 88th annual meeting of Transportation Research Board, Washington, DCGoogle Scholar
  36. 36.
    Xinhuanet.com (2008) Four missing after cargo vessel hits bridge in China. http://news.xinhuanet.com/english/2008-03/27/content_7868845.htm. Accessed 10 Oct 2011
  37. 37.
    Yang JD, Lu JJ, Gunaratne M, Dietrich B (2006) Modeling crack deterioration of flexible pavements: comparison of recurrent Markov chains and artificial neural networks. Transportation research record no. 1974, pp 18–25Google Scholar
  38. 38.
    Yokota H, Komure K (2004) Estimation of structural deterioration process by Markov-chain and costs for rehabilitation. Life-cycle performance of deteriorating structures. Third IABMAS workshop on life-cycle cost analysis and design of civil infrastructure systems and the JCSS workshop on probabilistic modeling of deterioration processes in concrete structures. Lausanne, Switzerland, pp 424–431Google Scholar
  39. 39.
    Zhang Y, Fan ZP, Liu Y (2010) A method based on stochastic dominance degrees for stochastic multiple criteria decision making. Comput Ind Eng 58:544–552CrossRefGoogle Scholar

Copyright information

© Springer India 2013

Authors and Affiliations

  1. 1.School of Civil EngineeringPurdue UniversityWest LafayetteUSA

Personalised recommendations