Asset Management Data Collection for Supporting Decision Processes

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2005-06-09
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Virginia Tech
Abstract

Transportation agencies engage in extensive data collection activities in order to support their decision processes at various levels. However, not all the data collected supply transportation officials with useful information for efficient and effective decision-making.

This thesis presents research aimed at formally identifying links between data collection and the supported decision processes. The research objective identifies existing relationships between Asset Management data collection and the decision processes to be supported by them, particularly in the project selection level. It also proposes a framework for effective and efficient data collection. The motivation of the project was to help transportation agencies optimize their data collection processes and cut down data collection and management costs.

The methodology used entailed two parts: a comprehensive literature review that collected information from various academic and industrial sources around the world (mostly from Europe, Australia and Canada) and the development of a web survey that was e-mailed to specific expert individuals within the 50 U.S. Departments of Transportation (DOTs) and Puerto Rico. The electronic questionnaire was designed to capture state officials' experience and practice on: asset management endorsement and implementation; data collection, management and integration; decision-making levels and decision processes; and identified relations between decision processes and data collection. The responses obtained from the web survey were analyzed statistically and combined with the additional resources in order to develop the proposed framework and recommendations. The results of this research are expected to help transportation agencies and organizations not only reduce costs in their data collection but also make more effective project selection decisions.

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Asset Management, Data Collection, Project Selection, Decision levels, Decision Processes, Web Survey
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