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Scoping and Conducting Data-Driven 21st Century Transportation System Analyses
  • Published Date:
    2017-01-01
  • Language:
    English
Filetype[PDF-6.02 MB]


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Scoping and Conducting Data-Driven 21st Century Transportation System Analyses
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  • Abstract:
    Improvements in commercially supported analytic software, increasingly powerful advanced computational platforms and a newly data-rich environment enables the 21st Century analyst to model large and dynamic surface transportation systems. However, a case can be made that the gains made by the individual analyst have outstripped the gains made by the organizations that manage transportation systems to capitalize on new analytic techniques. These analysts remain largely isolated from the mission of improving surface transportation system performance. Often, projects are defined and delivered to the analyst as an accomplished fact. The analyst is not frequently involved in diagnosing transportation system problems or using data to assist in analytic project scoping. Data and models developed for past projects are discarded, lost, or documented so poorly that they cannot be leveraged for future projects. With rare exceptions, there is a lack of advanced institutional models to systematically and consistently leverage the power of transportation analytics embedded within the broader transportation system management mission. This guidance document defines a Continuous Improvement Process (CIP) to integrate data-driven time-dynamic operational analyses within transportation systems management, featuring: System Diagnostics: Systematic methods for generating, integrating and prioritizing candidate analytical projects using a portfolio management approach. Project Scoping: Integrated analytical project and data acquisition scoping procedures (including a scoping tool). Data Preparation: Management practices for the preparation and analysis of integrated transportation system performance data with contextual data for the identification of operational conditions. Analysis: Best practices for data-driven analytical project execution. Management of Analytical Capital: A standard documentation procedure that supports project continuity from inception to lessons learned and preserves analytical capital (data and models) for the benefit of future analyses.
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