Budget Allocation Models for Pavement Maintenance and Rehabilitation: Comparative Case Study

Abstract

A principal component of maintenance and rehabilitation (M&R) planning is the allocation of limited funds to candidate projects to achieve optimal levels of system performance. This paper objectively compares three methods for budget allocation: cost-benefit analysis (CBA), integer-linear programming (ILP), and a “decision tree + needs-based” allocation. The study first presents a review of the major resource allocation approaches in the extant literature. It then implements, through a numerical case study, a representative method from each allocation approach. These are implemented on a subset pavement (5 sections) network for projects prioritization and budget allocation. The results indicate that, compared with the optimization model, both the CBA and the decision tree + needs-based allocation methods lead to faster declines (over the planning horizon) in average network condition scores (CS) (1% annually). However, this result arises due to both models inherently considering more “equity in outcome,” which is evidenced by a decreasing gap between the individual CS of pavement sections over time. The method leading to the highest average network performance (.3% decrease annually) is the integer-linear program. This method performs the worst in equity considerations. The findings from this study highlight the important dynamics of “equity-effectiveness” trade-offs inherent in different budget allocation methods for M&R programming. This paper also supports the need to develop more hybrid approaches capable of leveraging the merits of different resource allocation approaches. For practitioners, this work presents a consolidated view of the strengths and weaknesses of major resource allocation methods, which can aid in the transition that many highway agencies are making toward the use of more formalized analytical models for M&R budget allocation.