A federal rule forces the agencies to perform a Life-Cycle Cost Analysis (LCCA) for major highway pavement rehabilitation projects that account for the agency and future maintenance costs. For instance, TxDOT mandates the implementation of a LCCA for significant highway pavement construction projects with greater than 30 percent trucks or 100,000 average daily traffic volumes. The following three critical problems in the current LCCA practice are observed: 1) existing methods like RealCost require too many intuitive assumptions; 2) existing methods are time-consuming; and 3) the accuracy of the analysis performed under the numerous unrealistic assumptions is highly questionable. This project aims at developing a groundbreaking decision-support computer model for quantifying the most realistic, reliable life-cycle costs that account for the total project cost, the future maintenance cost and the road user cost within a viable integration analysis framework using a series of K-means cluster-driven regression analyses based on the big data gathered through the Pavement Management Information System (PMIS). This research effort is the first of its kind, as the existing tools including RealCost require many intuitive assumptions and judgments. The proposed decision-support model has the great potential to assist state transportation agencies in carrying out a LCCA with the reliable estimation of agency and road user costs. A more accurate life-cycle cost can help STAs make better-informed decisions when selecting the most feasible project scenarios among numerous alternatives, which can translate to better use of public funds. Estimating long-term maintenance and road user costs is the cornerstone of the LCCA. Therefore, methods developed from this project have great potential to improve the accuracy of LCCA.

• GIS-based urban sustainability model to assess the impact of LEED sustainable site credits.

• Economic impact analysis of LEED public transportation accessibility.

• Modeling future highway maintenance costs to support a life-cycle cost analysis for major infrastructure improvement projects.

• Economic Input-Output Life-Cycle Assessment (EIO-LCA) of highway pavement rehabilitation alternatives to assess sustainable energy use, external costs, and toxic emissions.