Browsing by Subject "Data quality"

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    Improve Traffic Volume Estimates from MnDOT's Regional Traffic Management Center
    (Minnesota Department of Transportation, 2020-02) Kwon, Taek M.
    The Regional Transportation Management Center (RTMC) at the Minnesota Department of Transportation (MnDOT) deploys a large number of traffic detectors in the Twin Cities' freeway network and continuously collects traffic data. While RTMC mainly uses the data for traffic and incident management, the TFA (Traffic Forecasting and Analysis) office uses the same data for monitoring, forecasting, planning, and reporting of transportation applications. RTMC provides current and historical volume data generated from its freeway network, but it does not provide quality information on that data. The objective of this project was to develop a new tool that can quickly explore the quality of detector data. To allow exploration of data quality, 13 detector-health parameters were computed using raw volume and occupancy data and then they were stored in a relational database. The final detector-health system was implemented as a client server-based system, in that a single server served many remote clients through the Internet. This report provides descriptions of the detector-health parameters, principles applied, server implementation, client software, and some analyses and application examples.
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    Traffic Data Quality Verification and Sensor Calibration for Weigh-In-Motion (WIM) Systems
    (Intelligent Transportation Systems Institute, Center for Transportation Studies, University of Minnesota, 2012-08) Liao, Chen-Fu; Davis, Gary A.
    Many state departments of transportation have been collecting various traffic data through the Automatic Traffic Recorder (ATR) and Weigh-in-Motion (WIM) systems as outlined in the Traffic Monitoring Guide (TMG) published by USDOT. A pooled fund study led by MnDOT was conducted in 2002 to determine traffic data editing procedures. It is challenging to identify potential problems associated with the collected data and ensure data quality. The WIM system itself presents difficulty in obtaining accurate data due to sensor characteristics, complex vehicle dynamics, and the pavement changes surrounding the sensor over time. To overcome these limitations, calibration procedures and other monitoring activities are essential to data reliability and accuracy. Current practice of WIM calibration procedures varies from organization to organization. This project aims to understand the characteristics of WIM measurements, identify different WIM operational modes, and develop mixture models for each operation period. Several statistical data analysis methodologies were explored to detect measurement drifts and support sensor calibration. A mixture modeling technique using Expectation Maximization (EM) algorithm and cumulative sum (CUSUM) methodologies were explored for data quality assurance. An adjusting CUSUM methodology was used to detect data anomaly. The results indicated that the adjusting CUSUM methodology was able to detect the sensor drifts. The CUSUM curves can trigger a potential drifting alert to the WIM manager. Further investigation was performed to compare the CUSUM deviation and the calibration adjustment. However, the analysis results did not indicate any relationship between the computed CUSUM deviation and the calibration adjustment.