Implementing the Freight Transportation Data Architecture: Data Element Dictionary (2015) / Chapter Skim
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1 1.1 Introduction U.S. state and metropolitan planning agencies are now expected to incorporate freight demand into their strategic transportation policies.
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2 Implementing the Freight Transportation Data Architecture: Data Element Dictionary • Different expansion factors and control totals • Differing procedures used for data aggregation or disaggregation • Difficulty in obtaining proprietary data from private sources • Inconsistency of data across different modes of transport • Inconsistency of data collection efforts across different modes of transport (rail versus highway versus air cargo versus intermodal) • Inaccurate or nonexistent local-level commodity flow data • Different vehicle classifications • Different data storage formats and dictionary definitions Slight or subtle variants in data definitions and metadata structures across datasets, and sometimes temporally within the same data sources, pose challenges to the compilation and use of freight data.
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Background 3 a typology across databases so that similar data elements could be identified. Classifying similar data elements facilitated the identification of differences in their definitions and aided in the development of harmonization or statistical bridges, as appropriate, for resolving those differences.
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