Estimating cost per lane mile for routine highway operations and maintenance
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- Midwest Regional University Transportation Center
- Jan 2011
Road markings; Pavement performance; Cost estimating; Highway maintenance; Distributions (Statistics); Vegetation; Trend (Statistics); Maintenance management; Traffic signs; Costs; Regression analysis; Road shoulders
- The disparity between maintenance budgets and maintenance requirements causes agencies to make difficult choices about maintenance priorities. There is a growing need to effectively link maintenance costs and condition to provide clear evidence of impacts due to budget tradeoffs decisions. The focus of this research was to develop mathematical relationships between expenditures for highway maintenance and the resulting maintenance condition by analyzing historic cost and condition data. The research involved analyzing maintenance condition and cost data over three years, 2004, 2005, and 2006 from the state transportation agencies in Michigan, Ohio, and Wisconsin for highway components including pavement, shoulders, roadside vegetation, drainage, signs, and pavement markings. The maintenance management systems at the states use different categorization schemes for their maintenance activity costs and different rating systems for maintenance condition, thus direct comparison between states was not possible. The primary result of this research is a set of probabilistic distribution functions for annual maintenance costs for a wide range of maintenance activities. Confidence intervals can be constructed around the average using the chosen level of confidence (i.e., 95%). The functions are useful for sensitivity and simulation analyses. The researchers hypothesized that data would reveal relationships between cost and condition. A regression tree analysis approach was used to search for relevant model equations. However, the statistical analysis of the data revealed weak evidence of these relationships. This finding is common for all three of the states that were investigated. There are at least two clear limitations of the data. First, to see trends over time, three years of data may not be enough. Furthermore, even with budget cuts, noticeable deterioration in condition, deficiencies, or maintenance backlog may take longer than three years. Second, the available cost and condition data are aggregated over many highway miles. Maintenance management and cost records generally do not include precise highway locations where maintenance was performed, the specific activities that were performed, nor the precise cost and timing of those activities. Consequently the trends and relationships between cost and condition are ?washed out? by the lack of precision.
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