Embedding Flexibility within Pavement Management: Technique to Improve Expected Performance of Roadway Systems

Abstract

Pavement-management systems are important tools that planning agencies depend on to cost-effectively maintain their roadway systems. The incorporation of uncertainty (beyond that related to pavement degradation) within these systems is limited, causing decision-makers to be unable to (a) recognize the risk of their status quo maintenance policy and (b) appreciate the value of embedding flexibility to capitalize on a prosperous future or mitigate downside risk. Consequently, this research develops a stochastic simulation model for pavement management that accounts for uncertainty as it relates to both pavement deterioration and the future cost of maintenance actions. The stochastic simulation model includes an allocation algorithm for sequential decision-making for the large-scale, performance maximization problem that generally leads to a high-fidelity solution. The authors implement the full model in a real-world case study in which they evaluate the implication of a planning agency broadening the types of paving materials and designs that it uses to proactively deal with an uncertain future. While the deterministic model estimates that an agency can achieve its objectives at a 4% cost reduction using a broader range of technologies, the probabilistic model estimates an expected cost reduction of 10%. This difference stems from the ability of the probabilistic model to capture the value generated from the flexibility to alter investment strategies at moments of spiraling costs for some maintenance actions and suppressed price levels for others. These findings suggest that the benefit from incorporating a diverse range of paving materials and designs by a planning agency could be much higher than agencies realize using the current deterministic frameworks.