Fast Detection and Prediction of Slippery Roadway Conditions for Enhanced Safety

Black ice, a nearly invisible hazard, contributes to over 10% of weather-related crashes in the U.S., causing 200,000 annual accidents, 700 fatalities, and 65,000 injuries. Traditional methods for detecting black ice involve fixed sensors and signs, but new vehicle-based technology offers cost-effective real-time data. However, obtaining comprehensive road condition data during inclement weather remains expensive and risky. State agencies must collect pavement surface data for asset management, yet the relationships between surface characteristics, weather conditions, and ice formation are not adequately understood. Research is needed to predict slippery conditions using existing data. Prediction of slippery conditions can be potentially more critical than detecting slippery conditions due to changing climates and weather extremes. This project aims to develop predictive models for slippery road conditions by collecting data with Mobile Advanced Road Weather Information Sensors (MARWIS) and Pave3D 8K on roadway segments before, during, and after inclement weather. These prediction models can then be applied to identify potentially slippery areas across Oklahoma, using the annually collected PMS datasets by ODOT. The aforementioned goals will be achieved through four tasks: Task 1: Data Collection: Use MARWIS technology to measure road conditions, including temperature, humidity, and road state. This data will be collected on selected testing sites based on weather forecasts and in collaboration with ODOT; Task 2: Surface Characteristics: Assess field friction values and collect pavement surface characteristics data using the Grip Tester and Pave3D 8K technology to understand their impact on road slipperiness; Task 3: Slippery Road Prediction Models: Leverage data from MARWIS and surface characteristics and create predictive models using statistical and machine learning methods for forecasting road conditions during rainy or icy days; Task 4: Implementation: Incorporate statewide surface characteristics data from ODOT into the predictive models, presenting results in a Geographic Information System (GIS) database for better situational awareness and road maintenance support. Expected outcomes include the following: (1) Integrated sensors for detecting slippery road detection; (2) Fast detection process using ODOT's data; and (3) a GIS database for slippery-prone roads. The proposed fast detection and prediction of slippery roadway conditions can directly result in a reduced number of motor vehicle accidents and decreased crash severity levels in terms of injuries and fatalities under inclement weather conditions.


  • English


  • Status: Active
  • Funding: $144876
  • Contract Numbers:



  • Sponsor Organizations:

    Office of the Assistant Secretary for Research and Technology

    University Transportation Centers Program
    Department of Transportation
    Washington, DC  United States  20590
  • Managing Organizations:

    Southern Plains Transportation Center

    University of Oklahoma
    201 Stephenson Pkwy, Suite 4200
    Norman, OK  United States  73019
  • Project Managers:

    Dunn, Denise

  • Performing Organizations:

    Oklahoma State University, Stillwater

    School of Civil & Environmental Engineering
    Stillwater, OK  United States  74078
  • Principal Investigators:

    Qiang Li, Joshua

  • Start Date: 20231001
  • Expected Completion Date: 20240930
  • Actual Completion Date: 0
  • USDOT Program: University Transportation Centers

Subject/Index Terms

Filing Info

  • Accession Number: 01899358
  • Record Type: Research project
  • Source Agency: Southern Plains Transportation Center
  • Contract Numbers: 69A3552348306, CY1-OSU-02
  • Files: UTC, RIP
  • Created Date: Nov 15 2023 9:40PM