Asset Management Data Collection for Supporting Decision Processes - Preface
Asset Management is a strategic approach to the optimal allocation of resources for the management, operation, maintenance, and preAsset Management combines engineering and economic principles with sound business practices to support servation of transportation infrastructure (FHWA 1999). The concept of 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, advanced sensors, distributed databases, and spatial technologies. These technologies have enabled data collection and integration procedures necessary to support the comprehensive analyses and evaluation processes needed for Asset Management. However, in many cases, 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 very large amounts of data and create vast databases that have not always been useful or necessary for supporting decisionmaking processes.
Although agencies have placed a large emphasis on collecting and integrating data, little effort has been placed on linking the data collection to the agencies' decisionmaking processes. The objective of the investigation discussed in this report was to investigate how State departments of transportation are linking their data collection policies, standards, and practices to their Asset Management decisionmaking processes, especially for project selection.
A literature review showed that the majority of the transportation agencies in the United States and the rest of the world have endorsed the concept of Asset Management. Many agencies in different regions of the world are working on the implementation of individual management systems, integrated infrastructure management systems, or Asset Management initiatives. The number of transportation agencies that are beginning to adopt, support, and implement the concepts and methodologies of Asset Management is rapidly increasing; however, Asset Management implementation is still in its initial stages, and there are many hurdles to overcome.
Asset Management implementation efforts have focused mainly on the overarching strategic and network (program) levels. Data needs for this type of decision comprise aggregated overall network performance indexes and overall network characteristics (i.e., overall interstate mileage, total number of bridges, etc.). Similarly, there have been advances in the project level of decisionmaking with the implementation of one or more individual management systems. Data needs for these types of decisions are project specific and require detailed inventory, condition, and performance data. The information gathered at this level is usually collected for only a reduced number of assets identified as the ones needing work, usually from the network-level analysis.
Last, the utmost implementation efforts have created common databases to minimize data storage and enhance interoperability between different management systems. These efforts do not usually address any particular decisionmaking level per se but contribute to the enhancement of the underlying foundations of all of them, which are the data and their corresponding issues of storage, analysis, etc. No efforts in practice, however, have been reported that aim at an overall system or network optimization. The optimization focus has been restricted to the various individual systems, although the notion of overall system integration can be found profusely in the literature.
The literature review also revealed that the level that has received the least attention, in terms of its data needs, is the project selection level. This level, however, is of vital importance to the overall success of the management as it links the overall network with the individual, specific projects. Furthermore, project selection traditionally has been made between projects belonging to the same asset class. Asset Management encourages cross-asset comparisons between the candidate projects for selection. This obviously has increased the data needs and also has created the need for the identification and use of effective and unbiased selection methodologies that can be applied to all different asset classes.
To complement the literature review, a survey was developed and distributed to capture the current level of Asset Management endorsement and implementation, as well as specific aspects of their data collection practices and their relationship with the project selection level of decisionmaking. The survey confirmed that transportation Asset Management implementation in the United States and around the world is still in its initial steps but that most transportation agencies are planning the integration of their individual management systems' roadway inventories and databases.
Transportation agencies have explicitly defined decisionmaking levels and are moving forward to a rationalization of their data collection activities. Data collection decisions are still predominantly based on past agency practices and personnel experience. There is, however, a significant trend toward use of data collection standards and input needs of management systems or processes behind the rationalization of data collection. In the particular area of project selection, there also seems to be a formally established relationship between the data collected and the decisions supported. However, Asset Management practitioners in general agree that project selection criteria cannot or should not be uniform and consistent for all asset types considered.
By using the collected information, the research team identified four States for indepth case studies. The research team then met with these agencies to explore in detail the linkages between data collection and decision processes and to document their practices. The agencies selected have used different data collection approaches and represent different degrees of Asset Management implementation.
The case studies indicated that there is no one-size-its-all approach for Asset Management data collection. The most appropriate approach will depend on the agency's needs and culture, as well as the availability of economic, technological, and human resources. A gradual implementation of the data collection efforts appears to be the most appropriate approach. Most of the data collected are currently being collected separately for individual asset types that support decisions within their corresponding silos. However, as the agencies embrace the Asset Management philosophy, there is an increased need for more consistent data collection among asset types and locations.
All agencies reviewed have a decentralized approach to project selection in which the field offices of the agencies decide which projects to execute. These agencies also have defined desired levels of service for the various assets and use those levels to enhance both the service provided to the user of the managed assets and the accountability of the decisionmakers. However, except for the Maryland State Highway Administration, the agencies do not have specific software tools for supporting project selection decisions at the field offices.
Finally, a data collection framework for project selection is recommended to optimize the data collection activities for project selection. The process provides clear, logical steps toward the complete rationalization of the data needs for these decisions. This framework can function as a starting point for transportation agencies that wish to handle project selection in a more systematic way and reduce costs by optimizing their data collection to support project selection decisionmaking. However, the proposed framework would only lead to partial optimization of an agency's data collection activities. It only addresses project selection decisions without considering the needs of the other levels of decisionmaking that might require overlapping or complementary data and hence necessitate new or extended data collection activities. A similar rationale can be defined for other levels of Asset Management decisionmaking.
Further research in the area of project selection data collection should be undertaken to determine the factors that render project selection criteria incapable of handling cross-asset comparisons. Additional effort also is needed to generalize the proposed data collection framework for an overall data collection optimization, taking into account all agency decision levels.
This research can help transportation agencies tailor their data collection activities according to their real decisionmaking needs. In this way, the research contributes both to the reduction of data collection costs and to a more effective and efficient implementation of Asset Management in its everyday practice. By focusing on the use of the data, the needs of the decision levels, and the processes to be supported, transportation agencies could define which assets and which data about these assets are most important for decisionmaking and tailor their data collection accordingly.