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6.0 Conclusions and Recommendations

Data quality is directly based on the extent to which a data set satisfies the needs of the person judging it. A better understanding and means to assess the quality of data offers various benefits including confidence and efficacy in decisions based on data. This project developed a framework and guidelines for measuring and assessing the quality of traffic data for different applications. The framework is comprehensive in providing alternative methods and tools for calculating the six fundamental data quality measures that would allow traffic data collectors and users to determine the quality of traffic data they are using, providing, sharing. The case studies used to illustrate the application of the framework are selected to represent a diverse range of data sources and applications. The guidelines include guidance on quality targets, levels effort required to establish a data quality assessment system within an agency, approaches for including metadata with data quality, and standards for data sharing agreements. The examples for metadata and proposed standards for data sharing agreements provide useful guidance in those areas.

The beta testing although limited has provided the opportunity to validate the concepts and methodologies presented in the framework and also validate some straw man estimates of data quality targets and estimates of the levels of effort. Overall, feedback from the beta test indicates that data quality assessment is important and that the framework provides necessary and useful tools to measure and assess the quality of traffic data.

The estimated levels of effort and quality targets need to be tested and validated based on actual experiences in the use of the framework and guidelines. Even though these have been validated through limited beta testing, more extensive validation is recommended.

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