Models for Predicting RWIS Sensor Misalignments and Their Causes

The Minnesota Department of Transportation uses the Road Weather Information System (RWIS) for monitoring the current weather and surface conditions of its highways. The real-time data received from these sensors reduce the need for road patrolling in specific locations by providing information to those responsible for directing winter maintenance operations. Since most road maintenance decisions and weather forecasts are explicitly dependent on the reliability and accuracy of the RWIS sensor data, it is important for one to be able to determine the reliability of the sensor data, that is, to determine whether a sensor is malfunctioning. In a previous project the authors investigated the use of machine learning techniques to predict sensor malfunctions and thereby improve accuracy in forecasting weather-related conditions. In this project, the authors used their findings to automate the process of identifying malfunctioning weather sensors in real time. The authors analyze the weather data reported by various sensors to detect possible anomalies. Their interface system allows users to define decision-making rules based on their real-world experience in identifying malfunctions. Since decision rule parameters set by the user may result in a false indication of a sensor malfunction, the system analyzes all proposed rules based on historical data and recommends optimal rule parameters. If the user follows these automated suggestions, the accuracy of the software to detect a malfunctioning sensor increases significantly. This report provides an overview of the software tool developed to support detection of sensor malfunctions.

  • Record URL:
  • Supplemental Notes:
    • This research was sponsored by the U.S. Department of Transportation, University Transportation Centers Program.
  • Corporate Authors:

    University of Minnesota, Duluth

    Department of Computer Science, 1114 Kirby Drive
    Duluth, MN  United States  55812

    University of Minnesota, Duluth

    Northland Advanced Transportation Systems Research Laboratory
    1303 Ordean Court
    Duluth, MN  United States  55812-3025

    Intelligent Transportation Systems Institute

    200 Transportation and Safety Building
    511 Washington Avenue, S.W.
    Minneapolis, MN  United States  55455

    Research and Innovative Technology Administration

    1200 New Jersey Avenue, SE
    Washington, DC  United States  20590
  • Authors:
    • Bhalekar, Prafulla
    • Crouch, Carolyn J
    • Crouch, Donald B
    • Maclin, Richard M
  • Publication Date: 2010-1


  • English

Media Info

  • Media Type: Web
  • Edition: Final Report
  • Features: Figures; Maps; Photos; References; Tables;
  • Pagination: 58p

Subject/Index Terms

Filing Info

  • Accession Number: 01153218
  • Record Type: Publication
  • Report/Paper Numbers: CTS 10-01, CTS Project 2007038
  • Files: UTC, TRIS, USDOT
  • Created Date: Mar 24 2010 1:31PM