A pavement management system (PMS) is a strategic and systematic process to maintain and upgrade the road network. When funding is limited, it is very important to identify the best mix of road preservation projects that provides the maximum benefits to society in terms of overall life cycle cost of the road network. The most common factors that play an important role for identifying projects are the following: budget, traffic volume, Present Serviceability Index (PSI) and risk associated with selecting treatment types. This research develops an optimisation methodology for county paved roads that identify the best mix of preservation projects within budget, maximising traffic (passengers and trucks traffic) on treated roads, maximising the weighted average PSI, and minimising the risk. This methodology will facilitate a statewide implementation of PMS for counties in the state of Wyoming.

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In the state of Colorado, the Colorado Department of Transportation (CDOT) utilizes their pavement management system (PMS) to manage approximately 9,100 miles of interstate, highways, and low-volume roads. ree types of deterioration models are currently being used in the existing PMS: site-speci c, family, and expert opinion curves. ese curves are developed using deterministic techniques. In the deterministic technique, the uncertainties of pavement deterioration related to tra c and weather are not considered. Probabilistic models that take into account the uncertainties result in more accurate curves. In this study, probabilistic models using the discrete-time Markov process were developed for ve distress indices: transverse, longitudinal, fatigue, rut, and ride indices, as a case study on low-volume roads. Regression techniques were used to develop the deterioration paths using the predicted distribution of indices estimated from the Markov process. Results indicated that longitudinal, fatigue, and rut indices had very slow deterioration over time, whereas transverse and ride indices showed faster deterioration. e developed deterioration models had the coe cient of determination (R 2 ) above 0.84. As probabilistic models provide more accurate results, it is recommended that these models be used as the family curves in the CDOT PMS for low-volume roads.

The objective of roadway tolling in rural areas is often tied to revenue generation for roadway maintenance. Thus, rural pricing models should directly incorporate a pavement deterioration and maintenance model. However, the interactions between these models are not simple, because tolls cause traffic diversion, which in turn affects deterioration rates and forecasted revenue. This article describes a rural pricing model which calculates diversion endogenously with a network assignment model. This model captures deterioration rates and pavement condition in the toll-setter's objective function, maximizing long-run net present value of the highway infrastructure. A novel deterioration model is used which is particularly suitable for computational efficiency. The resulting model is discontinuous and nondifferentiable, and involves solving a combinatorial knapsack problem as a subproblem. Thus, a simulated annealing-based algorithm is presented to solve it, in the framework of a new solution method built upon partitioning the feasible region. A demonstration is made using a network representing the state of Wyoming (28 zones, 60 nodes, and 188 links). Sensitivity analyses reveal that although the locations for optimal tolling are relatively stable as demand changes, the revenue collected can vary substantially. Relatively simple models are used throughout for computational reasons, and future research should investigate strategies for incorporating more advanced pavement and network models.