Enhanced Friction Prediction Models Based on Field Texture Data

The increase of highway accidents during wet-weather conditions is a worldwide problem. With that in mind, ensuring adequate skid resistance during wet conditions is of utmost importance for public safety. However, budget and resource limitations and practical constraints of skid-measuring equipment prevent transportation agencies from measuring skid resistance on their whole network. To circumvent this issue, a prototype data measuring and collection system was developed alongside robust prediction models that can be used to estimate friction using pavement texture information with a higher degree of accuracy than ever before. This is primarily due to advances in laser technology and data acquisition capacity.  Texture and skid resistance data were collected on 15 highway sections with varying surface types within 60 miles from the city of Austin using the prototype. Stringent quality control was set in place to ensure that the best quality texture and skid data were used to develop the prediction models. Based on the most recent literature in the subject, fourteen different texture summary statistics were used to find the best prediction model for pavement skid resistance. The results from this study are robust and indicate that texture summary statistics have a statistically significant influence on the skid resistance of roads and, when used in the right combination, one can develop a model with predictive power on the order of 55 percent in R².

Language

  • English

Media Info

  • Media Type: Digital/other
  • Features: Figures; Photos; References; Tables;
  • Pagination: 22p

Subject/Index Terms

Filing Info

  • Accession Number: 01763466
  • Record Type: Publication
  • Report/Paper Numbers: TRBAM-21-02564
  • Files: TRIS, TRB, ATRI
  • Created Date: Dec 23 2020 11:01AM