Impact of Multiple Highway Funding Categories and Project Eligibility Restrictions on Pavement Performance

Abstract

In maintenance and rehabilitation (M&R) programming, most budget allocation models in literature often assume one principal budget that can be used for funding all the different projects undertaken by the decision-making entity. For most agencies, this is rarely the case. This study explores the impact of formulating the M&R budget allocation problem with multiple funding categories (or budgets) having stringent project eligibility constraints. For state highway agencies (SHAs), there are often several federal, state, and local funding sources for highway projects. There are also agency norms and regulations that restrain the eligible projects under each funding category. This study presents an integer–linear programming model that accounts for funding restrictions in M&R budgets to assess changes in network performance and M&R decisions for pavement assets. The model is implemented in a numerical case study representing a subset network consisting of 500 pavement sections. Findings from this study suggest that the projected performance of the M&R program is overestimated when there is the assumption of one “central” budget with no project eligibility constraints. This may lead to an increase in unplanned expenditures toward reactive M&R projects to meet performance targets. Furthermore, the findings also suggest that increasingly restrictive budgets lead to a lower network performance for the same aggregate budget. The sensitivity analyses conducted confirm that the results obtained are robust against variations in key input variables used in the model. These findings contribute to ongoing efforts toward incorporating pragmatic constraints in optimization models to enhance their utility for effective decision making.