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Highway maintenance and rehabilitation (M&R) planning is a multifaceted decision-making process that requires the consideration of multiple but often conflicting objectives. Traditionally, state highway agencies (SHAs) have focused on maximizing the condition of a pavement network or minimizing agency costs needed to keep the network at an acceptable physical and functional level of service. However, other objectives like minimizing the road user costs and greenhouse gas (GHG) emissions are seldom factored into the M&R decision-making process. In this paper, a mixed integer program that accounts for these three objectives is presented. The proposed model is then implemented on a subset network in Texas consisting of 100 pavement sections. The results demonstrate the Pareto-optimal relationships that exist between the road condition improvements, road user costs, and GHG emissions. Notably, there is a positive relationship between condition score improvements and the observed marginal GHG emissions from the case network implementation. Furthermore, this network's results indicate that GHG emissions from materials and construction contribute to the most significant proportion of the total estimated GHG emissions. The results of the sensitivity analysis confirm the observed trends in the network case examined and provide valuable insights concerning the impact of variability in the budget size. The principal contribution of this study is in providing an approach for highway decision-makers to perform trade-offs in road condition improvements, road user costs, and GHG emissions of alternate pavement M&R programs or policies. [ABSTRACT FROM AUTHOR]

The Texas DOT (TXDOT) is concerned about the widening gap between pavement preservation needs and available funding. Thus, the TXDOT Austin district pavement engineer (DPE) has investigated methods to strategically allocate available pavement funding to potential projects that improve the overall performance of the district and Texas highway systems. The primary objective of the study presented in this paper is to develop a network-level project-screening and ranking method that supports the Austin district 4-year pavement-management plan development. The writers developed candidate-project selection and ranking algorithms that evaluated pavement conditions of each project candidate using data contained in the Pavement Management Information System (PMIS) database, incorporated insights from Austin district pavement experts, and implemented the developed method and supporting algorithm. This process previously required weeks to complete, but now requires about 10 min, including data preparation and running the analysis algorithm, which enables the Austin DPE to devote more time and resources to conducting field visits, performing project-level evaluations, and testing candidate projects. The case study results show that the proposed method assisted the DPE in evaluating and prioritizing projects and allocating funds to the right projects at the right time. [ABSTRACT FROM AUTHOR]