Guidance for Developing a Freight Transportation Data Architecture (2010) / Chapter Skim
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Specific NCFRP Project 12 objectives included the following: • Develop specifications for content and structure of a national freight data architecture that serves the needs of public and private decisionmakers at the national, state, regional, and local levels; • Identify the value and challenges of the potential data architecture; and • Specify institutional strategies to develop and maintain the data architecture. The research team undertook the following activities to address these research needs: • Completed a review of systems, databases, and architectures that might be used as a potential reference for the development of a national freight data architecture; • Conducted surveys and follow-up interviews, interviews with subject matter experts, and a peer exchange with freight transportation stakeholders; • Developed a formal definition for a national freight data architecture; • Identified high-level categories of data architecture components; • Identified potential implementation approaches; • Developed a list of specifications for a national freight data architecture; and • Identified challenges and strategies related to the implementation of a national freight data architecture.
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, most respondents indicated that they use freight data to support the production of public-sector transportation planning documents. However, respondents also reported using data for various other freight-related applications, adding weight to the notion that the national freight data architecture should support various freight-related processes.
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National Freight Data Architecture Definition Taking into consideration the results of the literature review, as well as feedback from surveys, follow-up interviews, and the peer exchange, the research team developed the following generic definition for a national freight data architecture: The national freight data architecture is the manner in which data elements are organized and integrated for freight transportation-related applications or business processes. The data architecture includes the necessary set of tools that describe related functions or roles, components where those roles reside or apply, and data flows that connect roles and components at different domain and aggregation levels.
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National Freight Data Architecture Components The research team identified the following categories of components to include in the national freight data architecture: • Physical transportation components, • Cargo or freight, • Freight functions or roles, • Business processes, • Data sources, • Freight-related data, • Freight data models, • Freight data standards, and • User interface and supporting documentation. Figure 10 (shown previously)
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that might impede the successful implementation of the data architecture. Examples include the following: – Feasibility of different implementation approaches; – Data storage requirements; – Feasibility of updated data entry protocols to eliminate data redundancies and support standardized data entry procedures; – Conversion of commodity code classifications; – Data life cycle and usefulness to support the decisionmaking process by public and private stakeholders; – Variability in data quality control practices, which affect data accuracy, completeness, and timeliness; – Differences in terminology, data item definitions, and data implementations among freight data stakeholders; – Prioritization of data architecture components; – Integration between shipper and carrier data to characterize commodity movements properly; and – Data confidentiality and security concerns.
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From page 81... ...
Census Bureau for the use of survey instruments; – Emphasize data access, quality, reliability, confidentiality, and integrity; – Participate in the standards development process; – Create crosswalks to ensure compatibility of survey data internally over time and externally across other datasets; – Involve stakeholders early and often through a variety of mechanisms and technologies; and – Develop and implement professional capacity and training programs early. One of the strategies for implementation mentioned above is to develop and compare candidate data architecture concepts.
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