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Pages 79-83

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From page 79...
... 79 General Summary of Findings The synthesis of the practice on The Transit Analyst Toolbox focuses on data governance activities adopted by transit to support "analysis and approaches for reporting, communicating, and examining transit data." The toolbox consists of • Transit Service Data and Performance Measures • Transit Data Management: Data Collection and Management Tools • Transit Data Governance The findings related to these areas are summarized. Transit Service Data Transit service data acquisition and collection is aided by technologies to generate, collect, and analyze raw data, and to generate performance metrics.
From page 80...
... 80 The Transit Analyst Toolbox: Analysis and Approaches for Reporting, Communicating, and Examining Transit Data Current initiatives such as the international transit data conferences, TCRP SG-18 project, and TIDES are just beginning to address transit data quality and integration issues, though they do not generally address critical management and governance issues that help overcome some of the major challenges faced by transit. In summary, with the increasing service data volume and variety, many transit agencies turn to data management tools to support their integration and data archiving needs.
From page 81...
... Conclusions and Suggestions for Future Research 81 • Data Security Management -- Ensuring privacy, confidentiality, and appropriate access. • Reference and Master Data Management -- Planning, implementation, and control activities to ensure consistency of contextual data values with a "golden version" of these data values.
From page 82...
... 82 The Transit Analyst Toolbox: Analysis and Approaches for Reporting, Communicating, and Examining Transit Data • Integrating existing processes and people responsible for governance into the data governance framework. Except for UTA, this means that no new meetings were set up to govern data; existing operations meetings covered data issues when they arose.
From page 83...
... Conclusions and Suggestions for Future Research 83 benefit. Prediction algorithms implementing ML/AI require large historic or streaming real-time data sets to implement.

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