For this paper, sponsored by Caltrans and the Western States Rural Transportation Consortium, the research team conducted a survey of DOT practitioners in western states and performed a literature review on data quality within the transportation field. They documented best practices and formulated recommendations in the interest of analyzing and documenting existing best practices for data quality and the dissemination of traveler information.
The researchers identified the following best practices:
- Maintain standards for collected data. Quality data is important for safe, efficient operation of transportation systems. This is particularly important for the provision of traveler information, and given that the current operating environment is increasingly focused on performance measurements, accountability, and "smart" operation of roadways.
- Lower the overall workload where possible. Leverage automated procedures to identify obvious technical problems. Automated systems can flag issues such as stale CCTV images or invalid sensor readings quickly and reliably, which makes it more likely that they can be resolved before they impact the quality of traveler information. Examples of some automated processing techniques include time-stamping data, updating status messages for equipment, and performing validation on sensor readings. This also allows for reliability to be tracked as a metric.
- Ensure baseline standards for equipment. Field elements should be rigorously tested before they are brought online. Laboratory calibration can ensure sensors are calibrated to the same high standards.
- Maintain systems. Regular and adequate preventive maintenance for all field elements is essential. The ability to remotely calibrate and restart sensors and systems is particularly useful for lowering the cost of this effort.
- Minimize the impact of human error. During shift changes in a TMC, use checklists to review which elements and systems are up or down. Make sure staff responsibilities are clear and develop a consistent procedure for handling traveler information. Simplify manual data entry processes as much as possible.
- Avoid technical conflicts. When possible, operate systems redundantly to minimize the impact of outages and to provide multiple overlapping sources of information. This also allows for cross-validation. Make sure common formats such as time-stamps are consistent across all data sources.
- Establish a data governance model. The model should clearly define who owns what data, as well as uses and associated thresholds for the specified data. Relevant quality metrics should also be clearly defined, including how to determine that requirements are being met. If possible, statewide standards for data quality and calibration should be defined and adhered to.
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