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Policy implications of standalone timing versus holistic timing of infrastructure interventions: Findings based on pavement surface roughness

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  • Qiao, Julie Yu
  • Du, Runjia
  • Labi, Samuel
  • Fricker, Jon D.
  • Sinha, Kumares C.

Abstract

Transportation agencies routinely make repair decisions in numerous contexts which include the timing of different repair treatments over an extended period. The timing policy may be characterized either by a standalone manner that maximizes the outcome or utility of individual treatments at a given time, or by a holistic manner that maximizes the utility of all treatments together within a given horizon period. This study begins with a hypothesis that the effect of the sum of treatment prescriptions may be different from the sum of the effect of individual treatment prescriptions. The paper discusses separate practical applications where each of these two situations is pertinent. The study is based on the use of surface roughness as the indicator of pavement performance, and therefore, the framework includes the development of treatment-specific treatment performance jump and performance loss models in terms of roughness. The framework also includes identification of the optimal roughness thresholds for each pavement treatment as a standalone activity, followed by a determination of the thresholds for each treatment from a holistic standpoint. The latter involves the establishment of an optimal repair schedule over the pavement life cycle and recording the resulting roughness thresholds at which each treatment is recommended for application within the optimal schedule. Then the paper presents a case study to demonstrate application of the framework, and to compare the optimal-schedule thresholds versus the stand-alone thresholds. The case study involved fourteen treatment types, two surface material types (rigid and flexible) and three road functional classes. The results suggest that in this context of highway management, the optimal triggers associated with individual standalone repair treatments are not necessarily the same (and yield inferior outcomes) compared to the application thresholds of those treatments when they are considered within an optimized life-cycle schedule. The results can provide guidance to agencies as they examine or update their current roughness thresholds for each standard treatment and their long-term condition-based or time-based repair schedules. The paper’s framework and results can also help agencies assess the consequences of deviations from a repair timing policy (delayed or hastened applications) due to lack of funds and other causes.

Suggested Citation

  • Qiao, Julie Yu & Du, Runjia & Labi, Samuel & Fricker, Jon D. & Sinha, Kumares C., 2021. "Policy implications of standalone timing versus holistic timing of infrastructure interventions: Findings based on pavement surface roughness," Transportation Research Part A: Policy and Practice, Elsevier, vol. 148(C), pages 79-99.
    • Handle: RePEc:eee:transa:v:148:y:2021:i:c:p:79-99
    DOI: 10.1016/j.tra.2021.02.021
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    References listed on IDEAS

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    1. Li, Yuwei & Madanat, Samer, 2002. "A steady-state solution for the optimal pavement resurfacing problem," Transportation Research Part A: Policy and Practice, Elsevier, vol. 36(6), pages 525-535, July.
    2. Ouyang, Yanfeng & Madanat, Samer, 2004. "Optimal scheduling of rehabilitation activities for multiple pavement facilities: exact and approximate solutions," Transportation Research Part A: Policy and Practice, Elsevier, vol. 38(5), pages 347-365, June.
    3. Chootinan, Piya & Chen, Anthony & Horrocks, Matthew R. & Bolling, Doyt, 2006. "A multi-year pavement maintenance program using a stochastic simulation-based genetic algorithm approach," Transportation Research Part A: Policy and Practice, Elsevier, vol. 40(9), pages 725-743, November.
    4. Tsunokawa, Koji & Schofer, Joseph L., 1994. "Trend curve optimal control model for highway pavement maintenance: Case study and evaluation," Transportation Research Part A: Policy and Practice, Elsevier, vol. 28(2), pages 151-166, March.
    5. Yu, Bin & Lu, Qing & Xu, Jian, 2013. "An improved pavement maintenance optimization methodology: Integrating LCA and LCCA," Transportation Research Part A: Policy and Practice, Elsevier, vol. 55(C), pages 1-11.
    6. Qiao, Yu & Saeed, Tariq Usman & Chen, Sikai & Nateghi, Roshanak & Labi, Samuel, 2018. "Acquiring insights into infrastructure repair policy using discrete choice models," Transportation Research Part A: Policy and Practice, Elsevier, vol. 113(C), pages 491-508.
    7. Friesz, Terry L. & Enrique Fernandez, J., 1979. "A model of optimal transport maintenance with demand responsiveness," Transportation Research Part B: Methodological, Elsevier, vol. 13(4), pages 317-339, December.
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    1. Giuseppe Loprencipe & Salvatore Bruno & Giuseppe Cantisani & Antonio D’Andrea & Paola Di Mascio & Laura Moretti, 2023. "Methods for Measuring and Assessing Irregularities of Stone Pavements—Part I," Sustainability, MDPI, vol. 15(2), pages 1-21, January.

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