Advertisement

KSCE Journal of Civil Engineering

, Volume 17, Issue 6, pp 1302–1316 | Cite as

Criteria for the development and improvement of PMS models

  • Daeseok HanEmail author
  • Kiyoshi Kobayashi
Highway Engineering

Abstract

When road agencies seek to introduce, evaluate or improve their Pavement Management System (PMS), there is often confusion due to lack of a long-term PMS development strategy. In fact, many road agencies have relied on others’ experiences or ready-made software not well suited to their own PMS situation. Obviously, a PMS model should be developed step by step with a well-grounded long-term PMS development plan. As fundamental research on PMS, this paper aims to foster sustainable development of PMS models by suggesting criteria for the development of PMS. As contents of the criteria, 1) a general PMS framework, 2) the standardization of PMS capability level, 3) a definition of PMS functions and 4) data requirements and management are treated as the main focus of research. These criteria are expected to serve as a useful guideline for the initial introduction, self-examination, and extension of PMS capabilities. While this research may be usefully applied to individual cases, a much more important goal is to establish compatibility among PMS models. Mitigating heterogeneity among PMS models can greatly benefit the PMS world. In addition, the criteria could serve as a foundation for various undertakings in PMS research.

Keywords

criteria of PMS development PMS functions framework of PMS PMS assessment data requirements of PMS 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. American Association of State Highway and Transportation Officials (AASHTO) (1978). A manual on user benefit analysis of highway and bus transit improvements, American Association of State Highway and Transportation Officials, Washington, D.C., USA.Google Scholar
  2. Bennett, C. R. and Greenwood, I. D. (2000). Highway development and management series volume seven: Modeling road user and environmental effects in HDM-4, The World Road Association (PIARC), La Defense, France.Google Scholar
  3. Bhattacharya, R. and Majumdar, M. (2007). Random dynamical systems: Theory and applications, Cambridge University Press, New York, USA.CrossRefGoogle Scholar
  4. Bonney, R. S. P. and Stevens, M. F. (1967). “Vehicle operating costs on bituminous, gravel and earth roads in east and central africa.” Road Research Technical Paper No. 76, Ministry of Transport, London.Google Scholar
  5. Broten, M. (1996). Local agency pavement management application guide, Washington State Department of Transportation (WSDOT), Washington, D.C., USA.Google Scholar
  6. Chatti, K. and Zaabar, I. (2012). NCHRP report 720: Estimating the effects of pavement condition on vehicle operating costs, Transportation Research Board (TRB), Washington, D.C., USA.Google Scholar
  7. De Weille, J. (1966). “Quantification of road user savings.” World Bank Staff Occasional Paper No. 2, The World Bank, Washington, D.C., USA.Google Scholar
  8. Do, M., Han, D., Lee, J., and Lee, Y. (2007). “Economic analysis for road pavement maintenance by using HDM.” Journal of the Korean Society of Civil Engineering, KSCE, Vol. 27, No. 3D, pp. 311–323 (in Korean).Google Scholar
  9. Do, M., Han, D., Yoo, I., and Lee, S. (2006). “Performance and economic analysis for rut-resistance pavement considering life cycle cost.” Journal of the Korean Society of Civil Engineering, KSCE, Vol. 26, No. 5D, pp. 783–796 (in Korean).Google Scholar
  10. Federal Highway Administration (FHWA) (1998). Life-cycle cost analysis in pavement design: In search of better investment decisions, Federal Highway Administration, Washington, D.C., USA.Google Scholar
  11. Fwa, T. F. (2006). The handbook of highway engineering, Taylor & Fransis Group, LLC, London, UK.Google Scholar
  12. Goodman, A. S. and Hastak, M. (2006). Infrastructure planning handbook: Planning, engineering, and economics, American Society of Civil Engineers (ASCE) Press, McGraw-Hill Companies, Inc., Virginia, USA.Google Scholar
  13. Han, D. (2011). Development of open-source hybrid pavement management system for an international standard, PhD Thesis, Kyoto University, Kyoto, Japan.Google Scholar
  14. Han, D., Do, M., Kim, S., and Kim, J. (2007). “Life cycle cost analysis of pavement maintenance standard considering user and socioenvironmental cost.” J. of the Korean Society of Civil Engineering, KSCE, Vol. 27, No. 6D, pp. 727–740 (in Korean).Google Scholar
  15. Huang, Y. H. (2004). Pavement analysis and design, 2nd Edition, Pearson Education, Inc., Upper Saddle River, New Jersey, USA.Google Scholar
  16. Hudson, W. R., Hass, R., and Uddin, W. (1997). Infrastructure management: Integrating, construction, maintenance, rehabilitation and renovation, McGraw-Hill Companies, Inc., New York, USA.Google Scholar
  17. Jiang, Y., Saito, M., and Sinha, K. C. (1988). “Bridge performance prediction model using the Markov chain.” Transportation Research Record, Vol. 1180, pp. 25–32.Google Scholar
  18. Jido, M., Otazawa, T., and Kobayashi, K. (2008). “Optimal repair and inspection rules under uncertainty.” J. of Infrastructure System, ASCE, Vol. 14, No. 2, pp. 150–158.CrossRefGoogle Scholar
  19. Kobayashi, K., Do, M., and Han, D. (2010a). “Estimation of Markovian transition probabilities for pavement deterioration forecasting.” KSCE J. of Civil Engineering, KSCE, Vol. 14, No. 3, pp. 341–351.Google Scholar
  20. Kobayashi, K., Ejiri, R., and Do, M. (2008). “Pavement management accounting system.” J. of Infrastructure System, ASCE., Vol. 14, No. 2, pp. 159–168.CrossRefGoogle Scholar
  21. Kobayashi, K., Kaito, K., and Nam, L. T. (2010b). “Deterioration forecasting model with multistage Weibull hazard functions.” J. of Infrastructure System, ASCE., Vol. 16, No. 4, pp. 282–291.CrossRefGoogle Scholar
  22. Kobayashi, K., Kaito, K., and Nam, L. T. (2012). “A statistical deterioration forecasting method using hidden Markov model with measurement error.” Transportation Research-Part B, Vol. 46, No. 4, pp. 544–561.CrossRefGoogle Scholar
  23. Korea Institute of Construction Technology (KICT) (2006). Performance and economic analysis of super-pavement, Korea Institute of Construction Technology, Korea (in Korean).Google Scholar
  24. Lea International, N. D. (NDLI) (1995). Modeling road deterioration and maintenance effects in HDM-4, Final Report Asian Development Bank Project RETA 5549, N.D. Lea International, Vancouver, Canada.Google Scholar
  25. Lancaster, T. (1990). The econometric analysis of transition data, Cambridge University Press, New York, USA.zbMATHGoogle Scholar
  26. Ministry of Land, Transportation, and Maritime Affair (MLTM) (2009). A guidebook for investment of transportation facilities, Ministry of Land, Transportation, and Maritime Affair, Korea (in Korean).Google Scholar
  27. Ministry of Transportation of British Columbia (MTBC) (2005). MicroBENCOST guidebook: Guidelines for the benefit cost analysis of highway improvement projects in British Columbia, British Columbia Ministry of Transportation and Infrastructure, Victoria, Canada.Google Scholar
  28. Nam, L. T. (2009). Stochastic optimization methods for infrastructure management with incomplete monitoring data, PhD Thesis, Kyoto University, Kyoto, Japan.Google Scholar
  29. Nam, L. T., Thao, N. D., Kaito, K., and Kobayashi, K. (2009). “A benchmarking approach pavement management: Lessons from Vietnam.” J. of Infrastructure Planning Review, JSCE., Vol. 27, No. 1, pp. 101–112.Google Scholar
  30. Odoki, J. B. and Kerali, H. G. R. (2000). Highway development and management series: Volume four — Analytical framework and model descriptions, The World Road Association (PIARC), La Defense, France.Google Scholar
  31. Permanent International Association of Road Congresses (PIARC) (2000). Highway development and management series, The World Road Association, Vol. 1–7, La Defense, France.Google Scholar
  32. Public Works Research Institute (PWRI) (1981). “Study on pavement performance and vehicle traveling cost.” Civil Engineering Technology, Note 23, Public Works Research Institute (PWRI), pp. 577–582 (in Japanese).Google Scholar
  33. Shahin, M. Y. (2005). Pavement management for airports, roads, and parking lots, 2nd Edition, Springer Science+Business Media, LLC, New York, USA.Google Scholar
  34. Tsuda, Y., Kaito, K., Aoki, K., and Kobayashi, K. (2006). “Estimating Markovian transition probabilities for bridge deterioration forecasting.” J. of Structural Engineering and Earthquake Engineering, JSCE, Vol. 23, No. 2, pp. 241–256.CrossRefGoogle Scholar
  35. Uddin, W. and Torres-Verdin, V. (1998). “Service life analysis for managing road pavement in Mexico.” Proc., Fourth International Conference on Managing Pavements, Durban, South Africa, Vol. 2, pp. 882–898.Google Scholar
  36. Winfrey, R. (1963). Motor vehicle running costs for highway economic studies, 3131 North Piedmont St., Arlington, Virginia, USA.Google Scholar
  37. Yang, J., Gunaratne, M., Lu, J. J., and Dietrich, B. (2005). “Use of recurrent Markov chains for modeling the crack performance of flexible pavements.” J. of Transportation Engineering, ASCE., Vol. 131, No. 11, pp. 861–872.CrossRefGoogle Scholar
  38. Yang, J., Lu, J. J., Gunaratne, M., and Dietrich, B. (2006). “Modeling crack deterioration of flexible pavements: Comparison of recurrent Markov chains and artificial neural networks.” Transportation Research Record, Vol. 1974, pp. 18–25.CrossRefGoogle Scholar
  39. Yun, W., Park, M., Lee, S., and Yu, I. (2007), “Current situations and improvement way of pavement management system on Korean national highway.” Magazine of Korean Society of Road Engineers, Vol. 9, No. 3, pp. 59–67 (in Korean).Google Scholar

Copyright information

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

Authors and Affiliations

  1. 1.Highway Pavement Research Division, SOC Research InstituteKorea Institute of Construction TechnologyIlsanKorea
  2. 2.Dept. of Urban ManagementKyoto UniversityKyotoJapan

Personalised recommendations