Because of a lack of reliable performance prediction models, many state departments of transportation (DOTs) use a needs-based budgeting process, namely, annual budget requests. Allocations of funding across maintenance activities and districts are developed on the basis of pavement maintenance needs derived from pavement inventory and annual or biannual condition data. This allocation of funding across maintenance activities and districts is challenging and often involves negotiation and balancing. A decision support model is proposed for the optimization of short-term pavement preservation budgeting based on two proven operations research techniques: goal programming for handling multiple objectives and an analytic hierarchy process for priority setting under multiple criteria. The model simultaneously considers two incommensurable and conflicting objectives: maximization of the preservation effectiveness in terms of extended service life and minimization of the total preservation cost. Application of the model is demonstrated with a short-term pavement preservation budgeting problem for a decentralized state DOT with nine maintenance districts. The illustrative example reveals that the proposed model is practical for supporting needs-based budgeting.

The Virginia Department of Transportation (DOT) has been using the results of automated video distress surveys to develop a pavement maintenance budget based on a needs assessment. However, these data consist only of quantities of distress that are visually observable at the pavement surface; no information regarding the actual structural capacity of the pavement system is available. Therefore, it is likely that maintenance activities assigned to certain locations are not the optimal treatment because of conditions unseen at the surface. Previous research conducted at the Virginia Transportation Research Council (now the Virginia Center for Transportation Innovation and Research) developed a protocol to collect data on pavement structural capacity by using the falling weight deflectometer on portions of Virginia's Interstate system. Many U.S. state departments of transportation have performed similar network surveys with the falling weight deflectometer to develop structural data for their pavements. Such data may include the deflection, effective resilient modulus of the subgrade, in situ structural number, layer moduli, overall pavement moduli, deflection basin area, modulus of subgrade reaction, area under the pavement profile, and so on. As the Virginia DOT begins the transition from using the 1993 AASHTO Guide for Design of Pavement Structures to using the Guide for Mechanistic–Empirical Design of New and Rehabilitated Pavement Structures, in situ structural characterization of the pavement network is necessary to ensure that funding spent on pavement rehabilitation is used optimally. This study presents the results of a network-level falling weight deflectometer survey of Virginia's Interstate system and describes an implementation process in which the data are used in an updated decision tree structure. The results of this study can be used by pavement design and management engineers to ensure that maintenance funding is optimally spent and to develop condition forecasting tools to assist with future funding allocations based on the structural capacity of the pavement.

Highway agencies have intuitively known for years about the need for high-quality data in their pavement management systems. Until now, however, it has been difficult to arrive at the value of good data. In Virginia, pavement condition data for a large network of roads have been obtained from digital continuous videos and interpreted by using a semiautomated software process. A detailed quality assurance process has been developed and applied to achieve the desired high-quality data. The project includes quality assurance that has been carried out since the inception of the project, including the application of necessary adjustments in the data collection process, to ensure that quality data conforming to predefined standards are obtained. Further, during production the data are continually monitored with the goal of attaining high-quality data. When the volume of data is large as in the present case, continual application of a quality assurance process is vital not only to prevent major changes at any stage but also to provide data that are usable as they are available. The effects of a complete and comprehensive quality monitoring plan, including quality control, quality assurance, and an independent validation and verification, on pavement management data and the resulting budgetary estimates are quantified. Pre- and postindependent validation and verification results were analyzed to determine the effects of a comprehensive quality monitoring plan on pavement distress data collection.

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