Introduction
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Background
Asset Management is a strategic approach to the optimal allocation of resources for the management, operation, maintenance, and preservation of transportation infrastructure (FHWA 1999). Asset Management combines engineering and economic principles with sound business practices to support decisionmaking at the strategic, network, and project levels.
One of the key aspects of the development of Asset Management is data collection. The way in which transportation agencies collect, store, and analyze data has evolved along with advances in technology, such as mobile computing (e.g., handheld computers, laptops, tablet notebooks, etc.), sensing (e.g., laser and digital cameras), and spatial technologies (e.g., global positioning systems [GPS], geographic information systems [GIS], and spatially enabled database management systems). These technologies have enhanced the data collection and integration procedures necessary to support the comprehensive analyses and evaluation processes needed for Asset Management (Flintsch et al. 2004).
In many cases, however, the data collection activities have not been designed specifically to support the decision processes inherent in Asset Management. As a result, the use of the aforementioned technologies has led agencies to collect huge amounts of data and create vast databases that have not always been useful or necessary for supporting decision processes.
Objective
In order to support Asset Management, agencies must collect, store, manage, and analyze large amounts of data in an effective and efficient manner. Although agencies have strongly emphasized collecting and integrating data, little effort has gone into linking the data collection to the agencies' decision-making processes. By focusing on the use of the data and the needs of the decision levels and processes to be supported, transportation agencies could define which assets and which data about these assets are more important for decisionmaking and tailor their data collection accordingly.
The objective of the investigation discussed in this report was to investigate how State departments of transportation (DOTs) are linking their data collection policies, standards, and practices to their Asset Management decision-making processes, especially for project selection. This decisionmaking level functions as an intermediate stage between high-level strategic decisions and low-level, project-specific decisions.
Methodology
The investigation started with a comprehensive and thorough literature review in order to retrieve related experience from academic and industrial sources throughout the world. Several reports have documented current and past practices in the United States and Canada as well as Europe and Australia. The literature review summarizes the state-of-the-art and corresponding state-of-the-practice implementation efforts in Asset Management, decision-making, and data collection.
To complement the literature review, a Web survey was developed to capture the current level of Asset Management endorsement and implementation, as well as specific aspects of data collection practices and their relationship with the project selection level of decisionmaking. A link to the survey was distributed to the DOTs in all 50 States and Puerto Rico, and the responses were tabulated and analyzed.
By using the collected information, the research team identified four candidate States for indepth case studies. The research team then met with these agencies to document best practices and to explore in detail the linkages between data collection and the decision process.
The knowledge gained from these activities was used to develop a framework for effective and efficient data collection, particularly for project selection and the identification of major criteria and data attributes to this decisionmaking level. This research can help transportation agencies tailor their data collection activities according to their real decision-making needs. In this way the research contributes both to the reduction of data collection costs and a more effective and efficient implementation of Asset Management.
Acknowledgments
The investigation documented in this report was conducted under the sponsorship of the Federal Highway Administration (FHWA) and the Virginia Transportation Research Council (VTRC).
The authors would like to acknowledge the contribution of the many Asset Management experts that responded to the survey; their knowledge and expertise provided the foundation for this investigation. In addition, the following individuals shared their experiences and insights during the development of the case studies: Charles Larson, Virginia Department of Transportation; Jeff Smith, Peter Stephanos, Dana Havlik, and Michael Wetzel, Maryland State Highway Administration; Mesfin Lakew, District of Colombia Department of Transportation; and Lonnie Watkins and Thomas Goebel, North Carolina Department of Transportation.
Finally, the authors would also like to acknowledge the contribution of the other members of the Virginia team that worked in this project. Aristeidis Pantelias and Chen Chen worked on this project while pursuing graduate studies at Virginia Tech. The survey of practice comprised the main body of Mr. Pantelias' Master Thesis. Mr. Chen prepared the Web-based survey form and assisted with the documentation of the case studies. Susan M. Willis-Walton, Associate Director of the Virginia Tech Center for Survey Research, and Susanne Aref, Ph.D., Director of the Statistical Consulting Center at Virginia Tech, contributed to the development and analysis of the survey.