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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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    PDF
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    urn:sha256:16807d1f042eedc39919415ed1880d73d6abb12f1d130a5f82eda89ab5b0c08e
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    Filetype[PDF - 2.38 MB ]
File Language:
English
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