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FHWA > TPM > Resources > Evaluation and Economic Investment > IF-08-018 > Chapter 1. Literature Review (continued)

Asset Management Data Collection for Supporting Decision Processes

Chapter 1. Literature Review (continued)

Summary of the Literature Review

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.

Asset Management implementation efforts have focused mainly on the overarching strategic and network (program) levels. For example, there have been efforts to link Asset Management systems with strategic planning and with overall network improvements. Data needs for this type of decision comprise aggregated overall network performance indices and overall network characteristics, (e.g., overall interstate mileage, total number of bridges, etc.).

In a similar vein, there have been advances in the project level of decisionmaking with the implementation of one or more individual management systems; in other words, pavement management systems (PMS), bridge management systems (BMS), etc. Data needs for these types of decisions are project-specific and require detailed inventory, condition, and performance data. However, it should be noted that the information gathered at this level is usually on an as-needed basis. It is collected only for a reduced number of assets that have been identified as the ones needing work, usually from the network-level analysis. Strong emphasis has traditionally been placed on the data needs of project-level decisionmaking and will continue to be so, although these needs depend on and are most usually defined by the individual management systems and process employed at this decision level.

Last, the utmost of implementation efforts have been cases in which common databases have been or are being created to minimize data storage and enhance interoperability between different management systems. These efforts do not usually address any particular decisionmaking level per se, but they contribute to the enhancement of the underlying foundations of all levels, which are the data and their corresponding issues of storage, analysis, etc. No efforts, however, have been reported in practice aiming at an overall system or network optimization. The optimization focus has been restricted to the various individual systems, although the notion of an overall systems integration can be found profusely in the literature.

The literature review has revealed that although there has been progress and research in almost all levels of decisionmaking, the level that has received the least attention in terms of its data needs is project selection. This level, however, is of vital importance to the overall success of the management because it links the overall network with the individual, specific projects. Project selection has unique data needs: The data must be detailed enough to effectively assist the understanding and rationalization of project selection, and at the same time they must be aggregate enough to allow projects of different nature and scope within the entire network to be addressed. Therefore, this decisionmaking level requires data that are between being too general and too specific. General data would not help in the selection project because they would ignore vital project details, but it is usually not cost-effective to collect very detailed (i.e., project-level) data for the project selection process. Furthermore, project selection has traditionally been made between projects that belong to the same asset class. Asset Management encourages the broadening of this traditional practice by encouraging cross-asset comparisons between the candidate projects for selection. This has obviously increased the data needs and has also created the need for the identification and use of effective selection methodologies that are equitable and unbiased in their application to all different asset classes.

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Updated: 02/14/2013