Integrated planning and budget allocation for highway maintenance, rehabilitation, and capital construction projects

Highway infrastructure is one of the critical components of the infrastructure network needed for the socio-economic development of a country. However, increased urbanization, limited funds, the need to consider sustainability continue to challenge the planning process for developing and maintaining highway infrastructure. Accordingly, decision-makers are tasked with making optimal decisions while achieving the strategic goals set by federal, state, district, and/or local highway agencies. Pivotal to making such resource allocation decisions, is the availability and accuracy of asset-related data and planning constraints which can guide data-driven decisions to be made by State Highway agencies (SHAs). Currently, several decision-makers still depend significantly on subjective engineering judgment to make decisions on funds allocation. Hence, there is a need for more formal and logical approaches to resource allocation as well as evaluation metrics for conducting alternatives analysis. This notwithstanding, the development of multiple incompatible legacy systems and the presence of several funding categories with stringent project eligibility requirements underpins a “siloed” approach to planning for highway infrastructure. There are often multiple functional groups working on the same asset network but with heterogeneous information systems and distinct decision-making practices. This “siloed” approach can create inefficiencies in projects selection and lead to inter-project conflicts in the highway projects proposed by these different functional groups. When left unaddressed, these spatial-temporal conflicts among projects can result in the misuse of limited taxpayer dollars and ultimately, a lower performance of the network. To address these issues with budget allocation and integrated highway planning, this study contributes to the body of knowledge in three primary ways. First, the study provides a synthesized analysis of budget allocation methods and provides a comprehensive approach to evaluating the performance of different methods employed for M&R decision-making. Secondly, this study formulates and accounts for the impact of multiple funding categories and project eligibility restrictions in budget allocation models. The inclusion of this pragmatic characteristic of M&R decision-making demonstrates the inefficiencies that can result from having increasing restrictions on multiple funding categories. Thirdly, a shared ontology is developed to enable a dynamic link between planning information and projects information. The resulting formalized representation (ontology) was validated by using multiple approaches including automated consistency checking, task-based evaluation, and data-driven evaluation. An implementation tool was also developed and applied to an actual case study problem. The tool was validated by using a Charrette test and feedback from subject-matter experts.