Browsing by Subject "Errors"

Now showing 1 - 2 of 2
  • Thumbnail Image
    Item
    Models for Predicting RWIS Sensor Misalignments and Their Causes
    (University of Minnesota Center for Transportation Studies, 2010-01) Bhalekar, Prafulla; Crouch, Carolyn J.; Crouch, Donald B.; Maclin, Richard M.
    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 we investigated the use of machine learning techniques to predict sensor malfunctions and thereby improve accuracy in forecasting weather-related conditions. In this project, we used our findings to automate the process of identifying malfunctioning weather sensors in real time. We analyze the weather data reported by various sensors to detect possible anomalies. Our 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.
  • Thumbnail Image
    Item
    Simulated driver performance, error, and acceptance study of a J-turn intersection with 3 levels of signage
    (2024-01-08) Morris, Nichole L; Schwieters, Katelyn R; Tian, Disi; Craig, Curtis M; nlmorris@umn.edu; Morris, Nichole L; University of Minnesota HumanFIRST Lab
    Thirty-six participants with limited previous experience and knowledge of J-turn intersections participated in a simulation study to examine their acceptance of J-turns and left turning navigational performance at three simulated J-turn intersections in counterbalanced order, each featuring one of three signage levels (minimum, intermediate, and full). Participants navigational path was visualized and scored for error occurrence by 3 raters/coders. Eleven different error types occurred and they were classified as minor, moderate, or major severity errors. Participants provided demographic information, crash history, and acceptance of J-turn intersections (across three scales) before and after driving through the simulated J-turn intersections. The data has been deidentified and is available to provide a better understanding of common errors from drivers who are experiencing J-turn intersections for the first time and the resultant influence that their error experiences have on their acceptance of the novel intersection design.