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Chapter 3. Case Studies

By using the information collected from the survey of experts, the research team identified six candidate States for indepth case studies. After communicating with these six State DOTs and reviewing the information provided, four agencies were selected for further study in the second phase of the project. In this phase, the research team met with these agencies to explore in detail the linkages between data collection and the decision process and to document their practices. The agencies selected have used different data collection approaches and represent different degrees of Asset Management implementation.

The first case study covers an agency that is developing a comprehensive Asset Management system using mostly internal resources. It has tried several approaches for data collection and has used both the agency's personnel and consultants. The second case study examines an agency that has focused its system development and data collection efforts on separate engineering management systems for different types of assets and is working on the integration of these systems. In this case, the review focused on the data collection for two types of assets: Pavement and storm water management facilities. The third case study illustrates a different approach for asset management that relies heavily on the private sector support. The reviewed agency outsources most of the maintenance of their assets through performance-based contracts. Although consultants perform most of the data collection, the agency has also emphasized incorporation of citizen input on the asset evaluation process. Finally, the fourth case study focuses on data collection practices that support one of the components of the agency's Asset Management system, the maintenance management system (MMS). The reviewed agency has developed the system and conducted the initial data collection by using a consulting firm that specializes in Asset Management. These examples may provide useful guidance for other agencies planning to undertake similar efforts.

Virginia Department of Transportation (VDOT)

About 10 years ago, VDOT started to develop an integrated infrastructure management system, which has evolved into today's Asset Management system. The work has included data collection, system analysis, automatic reporting, prioritization, and optimization. The current system is the first step in a long-term process, which will include data collection, development of system-wide analysis tools, and implementation. The efforts included the establishment of an Asset Management division, which has used all available information to develop a roadmap to the future for Asset Management. This roadmap includes the identification of core business needs and provides directions for reaching future DOT goals.

Table 8 summarizes the main decisionmaking levels formally identified by VDOT. The Central Office provides information about network performance targets, budget constraints, policy (best practices), and recommended programming results to the district maintenance engineers (DME). There is no explicitly defined Statewide project selection level: Because the districts have more information about their own infrastructures, they decide on specific treatments and projects. A data envelope analysis is used to identify best practices.

Core Asset Management Business Processes

The following core business processes have been identified:

  • Condition assessment: This is conducted by the Central Office with support from the districts (pavement and bridges with random condition assessment [RCA] for the other most significant assets).
  • Needs-based budget (NBB): Financial modeling is conducted at the network level by the Central Office (Asset Management and information technology groups).
  • Project selection: This is conducted at the district level. The DME gets the needs-based budget and allocates the funds to specific projects. There are expert groups that have been appointed to develop and improve decision trees for the various asset classes.
  • Feedback: Web-based flexible feedback tools allow permanent recording of any user feedback.
  • Work accomplished and inventory updates: There is a module under development that will allow field crews to report their work by using PDAs or laptop computers.

These processes have not been formally associated with the decisionmaking levels included in table 8; however, several of them can be easily assigned as noted in their descriptions. VDOT is planning to address this issue in the near future.

Table 8. Decisionmaking level formally identified by VDOT.
Decisionmaking Level Definition
Strategic Level Decisions regarding cross-agency issues and funding allocation across the highway maintenance, mobility management, and operation programs.
Program Level Operation of the programs (i.e., highway maintenance) to achieve established State network performance targets.
Project Level Works related to district maintenance engineers, such as identiication of funding for various projects.

Needs-Based Budgets

The NBB is the most highly developed Asset Management module and is presented as an example in this section. The NBB is an interactive tool that helps VDOT identify performance targets and computes the level of funding required to obtain the defined performance. At present it is currently an exclusively network-level tool to help the agency identify Statewide maintenance programs sponsored or funded by the commonwealth highway maintenance program (approximately $930 million per year). The Asset Management division has defined a performance measure matrix with the desired performance levels.

It is hoped that in the future NBB also will be a project-level tool used by the districts to set and track their performance standards. The planned process includes developing a performance-based budget for achieving a certain level of performance, sharing these numbers with the districts, and holding the districts accountable; however, the implementation of this process at the district level is in very early stages.

The Asset Management division is developing a module for evaluating the district performance and measuring the condition of their highway assets. This will allow adjustment of the funding distribution between asset groups across a 6-year horizon. It is expected that VDOT will be able to report this information to the public.

Information Needs

The VDOT has assessed the information needed on the basis of the decision models supported. However, the assessment has not been conducted as a holistic process, but more for the various individual processes or models.

There are several sources of information (data streams) collected to produce the needs-based budget:

  • Pavements: The pavement condition data is gathered independently from the Asset Management system. There is a pavement management system group that performs condition surveys (at present mostly windshield survey and automatically collected profile data).
  • Bridges: The bridge information is collected to feed the PONTIS bridge management system. The information is requested by a Federal mandate for the National Bridge Inventory.
  • Other Highway Assets: The information about the other highway assets (eight types at the present time) is gathered through a simple strategy conducted Statewide. The RCA collects data from the assets that are most significant to the maintenance program budget. There is pressure from the field and management to increase the number of assets considered.

One of the lessons learned from the data collection efforts is that the agency needs to target the data collection to the most significant assets and to the decisions to be made. In the late 1990s, VDOT started a project that tried to capture every highway infrastructure asset on every highway Statewide. The project began in three counties as a pilot, and it was found to be too expensive and overwhelming. The current approach is to build the inventory over time.

Project Selection

The various maintenance and rehabilitation (M&R) strategies considered for the main asset types in the NBB have been defined by an expert panel composed of representatives from the Asset Management division and the districts. These panels have also defined the main selection criteria (and consequent data needs) and best practices decision trees. The districts can adjust the criteria on the basis of their knowledge and experience. The criteria are documented in the individual management systems (e.g., for pavement or bridges) and their respective guidelines and manuals. These criteria are used within the asset groups but not to evaluate tradeoffs among different asset classes. At present, VDOT does not conduct cross-asset tradeoff analyses.

Data Collection

Data only are collected for supporting decisions at the network level. Decisions on what data to collect are mainly on the basis of experience. The districts collect data and update information in a central database. The network-level data collection framework is revised annually. Project-level data collection policies (HTRIS) are quite mature. There are many offices involved with Asset Management data collection activities, more specifically, 9 district offices, 45 residences, and 246 area headquarters.

The information is updated annually and stored in a corporate database with a GIS platform. The pavement and bridge data are kept in separate databases that feed the central database once a year. The accuracy needed for the various data items has not been addressed objectively. However, there are good quality assurance procedures in place to check the information collected and input to the system. The agency is planning to conduct a sensitivity analysis to determine which data items have the most impact in the decisions.

The budgeting process defines the frequency of data collection. All the data is collected between January and May and revised during June and July to be used in August for the NBB. There are no good estimates of the data collection cost. The RCA costs approximately $60 per 0.1-mile segment, and the pavement data collection costs approximately $45 per kilometer.

Updated: 06/27/2017
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