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Evidence-based decision making : developing a knowledge base for successful program outcomes in transportation asset management.

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English


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  • Abstract:
    MAP-21 and AASHTO’s framework for transportation asset management (TAM) offer opportunities to use more ; rigorous approaches to collect and apply evidence within a TAM context. This report documents the results of a study ; funded by the Georgia Department of Transportation, conducted by the Infrastructure Research Group (IRG) at Georgia ; Institute of Technology, to link TAM investments to outcomes. The study methodology includes a review of evidence based approaches from various fields including healthcare, education and organizational management, an “evidence ; exchange” among transportation practitioners, and several case studies demonstrating how evidence can be gathered ; and used in TAM. The study identifies attributes of an evidence-based approach that can enhance AASHTO’s existing ; framework for TAM program advancement and decision making. These attributes are used to develop (1) a hierarchy ; of evidence that can inform evidence-based decision making (EBDM) for TAM, (2) a documentation framework for ; TAM that can be applied at the project and program levels to support evidence-based decisions, and (3) a prototype ; evidence database that can be used to accumulate the knowledge needed for evidence-based decision making in a TAM ; context. This study also applies evidence-based decision-making processes to provide guidance on how to develop ; higher quality evidence for TAM decision making. The documentation framework is used to synthesize and gather data ; on evidence related to TAM decision making, and this information was used to populate the TAM evidence database. ; The primary outcome of this work is a prototype TAM evidence database and resource for knowledge sharing that can ; assist transportation agencies in meeting the performance-based planning requirements of MAP-21.
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    Filetype[PDF - 2.38 MB]
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    urn:sha256:16807d1f042eedc39919415ed1880d73d6abb12f1d130a5f82eda89ab5b0c08e
File Language:
English
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