Integrate RTMC Vehicle Classification into the Current Detector Volume Data

Collection of vehicle classification data is considered an essential part of traffic monitoring programs. The objective of this project is to integrate the raw classification data generated by the Minnesota Department of Transportation (MnDOT) Regional Transportation Management Center (RTMC) into the existing volume data managed by the Traffic Forecasting and Analysis (TFA) Section under the Office of Transportation System Management (OTSM). RTMC manages a large number of traffic sensors in the Twin Cities’ freeway network and continuously collects a huge amount of traffic data. Recently, it added Wavetronix radar sensors, from which length-based classification and speed data are generated in addition to typical volume and occupancy data generated by loop detectors. This project integrates this classification data into the existing TFA volume data, which could save cost and time for TFA in the future by using existing classification data. The project team also integrated the RTMC speed data for the locations where it was available. The final deliverable of this project was a software tool called detHealth_app, from which users can retrieve classification and speed data in addition to volume/occupancy data in multiple formats including Federal Highway Administration (FHWA) format. The detHealth_app program was thoroughly tested and has been successfully used by MnDOT TFA.

  • Record URL:
  • Corporate Authors:

    University of Minnesota, Duluth

    Department of Electrical Engineering
    Duluth, MN  United States 

    Minnesota Department of Transportation

    Office of Research & Innovation
    395 John Ireland Boulevard, MS 330
    St. Paul, MN  United States  55155-1899
  • Authors:
    • Kwon, Taek M
  • Publication Date: 2020-11


  • English

Media Info

  • Media Type: Digital/other
  • Edition: Final Report
  • Features: Appendices; Figures; References; Tables;
  • Pagination: 55p

Subject/Index Terms

Filing Info

  • Accession Number: 01762811
  • Record Type: Publication
  • Report/Paper Numbers: MN 2020-31, CTS #2020018
  • Contract Numbers: 1003325 (wo) 125
  • Created Date: Dec 24 2020 11:43AM