Development and Validation of a Methodology to Estimate Retroreflectivity of Pavement Markings Using LiDAR

Details

• Creators:
  Mohammadi, Abbas ; Medina, Juan ; Rashidi, Abbas
• Corporate Creators:
  University of Utah. Department of Civil and Environmental Engineering
• Corporate Contributors:
  Center for Transformative Infrastructure Preservation and Sustainability (CTIPS) Region 8 University Transportation Center (UTC) ; United States. Department of Transportation. Federal Highway Administration ; Utah. Department of Transportation. Research and Innovation Division
• Subject/TRT Terms:
  Continuous And Dashed Lane Line Data Collection Lane Lines Laser Radar LiDAR Intensity Longitudinal Control Machine Learning Marking Materials Pavement Marking Retroreflectivity Pavements Reflective Longitudinal Roadway Treatment Reflective Signs

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• Publication/ Report Number:
  UT-26.15
• Resource Type:
  Tech Report
• Geographical Coverage:
  United States
• Edition:
  Final Report: Sep 2023 to Dec 2025
• Corporate Publisher:
  Utah Department of Transportation
• Abstract:
  Longitudinal pavement markings significantly affect traffic safety, particularly in adverse weather and nighttime conditions when crashes and fatalities are often overrepresented. Given the wide range of factors affecting the performance of pavement markings, periodic monitoring is needed to ensure their integrity and adequate retroreflectivity levels. Typical monitoring methods include individual readings from manual retroreflectometers and mobile setups where much larger segments can be covered in shorter periods. However, mobile setups require specialized equipment, calibration, and significant economic resources. This research uses LiDAR data collected as part of asset management efforts to help assess durable pavement markings, primarily longitudinal freeway lines. This project developed a framework to identify and isolate pavement markings from LiDAR point clouds, to filter and refine data, and to produce models and evaluate the associations between field-measured retroreflectivity and a combination of intensity from LiDAR readings, RGB data, and marking material. Freeway data was used to analyze the potential of LiDAR in assessing the retroreflectivity of pavement markings. Results show that LiDAR data can produce reliable associations to retroreflectivity levels both in terms of classification and value prediction to make maintenance decisions. A key component in the proposed methodology for prediction and classification is the clustering of individual assessments spatially correlated to produce robust segment-level outcomes. This study is complemented by a computer tool that implements the analysis methods and allows for processing of new datasets. In addition, a related project extends the work presented here to incorporate imagery into the analysis for more comprehensive assessments.
• Format:
  PDF
• Funding:
  24-8331
• Collection(s):
  US Transportation Collection
• Main Document Checksum:
  urn:sha-512:11350f3f731fd6d53c1df7b5ff01ef1ba1cfe38f18a3d4a50a4d7d732a42da214e15073c6ab1368f69f6bbc20d96caf45ecbdeb7fcf71fc3545301b9af5ba9c0
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/92659/dot_92659_DS1.pdf
• File Type:
  [PDF - 4.57 MB ]