Develop Efficient Prediction Model of Highway Friction on an Annual Basis on Texas Network [Project Summary Report]

Details

• Creators:
  Prozzi, Jorge ; Sabillon-Orellana, Christian ; Inoue, Danilo ; Li, Ruohan ; Hooton, Anna ; Huang, Robing ; Xu, Hongbin ; Hernandez, Joaquin
• Corporate Creators:
  University of Texas at Austin. Center for Transportation Research
• Corporate Contributors:
  Texas Department of Transportation. Research and Technology Implementation Office ; United States. Department of Transportation. Federal Highway Administration
• Subject/TRT Terms:
  • Pavement surface course
  • Friction
  • Skid resistance tests
  • Machine learning
  • Artificial intelligence
  • Data analysis
  • Regression analysis
  • Mathematical prediction
• Publication/ Report Number:
  0-7031-01
• Resource Type:
  Brief
• Geographical Coverage:
  United States ; Texas
• Edition:
  Project Summary Report
• Corporate Publisher:
  University of Texas at Austin. Center for Transportation Research
• Abstract:
  Measuring network skid resistance annually is essential for the safety of the road users and the public. However, current skid resistance measurement equipment is inefficient due to the large volumes of water required, limiting TxDOT to assessing at most one-third of the state-maintained network on an annual basis. While past efforts to estimate skid numbers based on texture had varied success, recent advancements at the University of Texas at Austin have shown that it is feasible to measure texture accurately in the field at highway speeds and to use those measurements to estimate pavement friction with an accuracy around 0.05 in terms of Skid Number (SN). This research project developed a system to measure texture at highway speeds and created a methodology to predict SN for the entire Texas network. The new system allows efficient, high-speed field measurements, enhancing TxDOT' operations and safety. This approach improves the accuracy of skid number predictions and enables comprehensive annual assessments, significantly advancing friction management and highway safety.
• Format:
  PDF
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/93506/dot_93506_DS1.pdf
• File Type:
  [PDF - 448.88 KB]
• Collection(s):
  US Transportation Collection
• Main Document Checksum:
  urn:sha-512:04b7c5d8fa796b6a1c4b8d9ddd44917dec16c9a1739f3196650f49dab7abc07a6ba813512e07369e1eaab3aa24fe5a025512cdb2c774aa39f1905ab7b79a726f