LiDAR Used to Estimate Pavement Marking Retroreflectivity
LiDAR Used to Estimate Pavement Marking Retroreflectivity
Item #: 20260081
Embedded Files
CONTACTS
Implementing Organization: Central Maintenance / Facilities Management
Implementation Lead: Abdul Wakil
Development Team: David Stevens, Abdul Wakil, Ryan Ferrin, Shawn Lambert, Chris Whipple, Benjamin Maughan, Ethan Turner, Dave Thomas, Brad Loveless, Robert Miles, Joni Demille
University of Utah
Abbas Mohammadi
Juan Medina
Abbas Rashidi
Article Written By: Abdul Wakil
Innovation Team Coordinator: Quinten Klingonsmith
STATUS
Implementation Date: December 1, 2025
Adoption Status: Fully Implemented
Adoptability Note: Please talk with Abdul Wakil for information about using LiDAR to assess your pavement marking's retroreflectivity.
APPLIES TO
Topic: Data Collection, Analysis and Visualization
Organization(s): Aeronautics, Central Maintenance / Facilities Management, Performance and Asset Management, Region 1, Region 2, Region 3, Region 4, Research and Innovation, Strategic Technologies, Structures, Traffic and Safety, Traffic Management Division, Transit and Trails
Job Role(s): Business Analyst, Materials Engineer, Program Director, Program Manager, Program Specialist, Roadway Operations / IMT
Tags: highway transportation, infrastructure preservation, labor productivity, employee empowerment, job satisfaction, occupational safety, crashes, injuries, expenses, expenditures, automation, ( process improvement ), tracking systems, governance > auditing, policy making, procedures, compliance, law, ( legal ), artificial intelligence ( AI ), ( data visualization ), LiDAR, data science, maintenance, testing > samples, sampling, traffic safety > highway safety, trucking safety, paint, retroreflectivity, pavement markings, quality of life, LiDAR intensity, machine learning, continuous and dashed lane line, reflective longitudinal roadway treatment
PROBLEM
PROBLEM
The ability for drivers to see lane lines on roads, especially at night and during incremental weather, is critical. The Federal Highway Administration (FHWA) requires transportation agencies to maintain minimum levels of pavement marking retroreflectivity on public roads. Retroreflectivity is the measure of the amount of light that returns from pavement markings to a driver’s eyes.
In recent years, vehicle-mounted retroreflectometers have been developed to measure retroreflectivity at highway speeds. Even with this advancement, it takes a considerable amount of time and expense to continually measure each state road.
UDOT already uses LiDAR (Light Detection and Ranging) to survey the entire highway network within a two-year cycle. While this survey is done for other purposes, the question was raised about whether it could also measure pavement marking retroreflectivity.
CHANGE
CHANGE
Central Maintenance commissioned a study through the Research and Innovation Division. A team from the University of Utah was selected. They developed and validated a methodology to estimate retroreflectivity of pavement markings using LiDAR (Report No. UT-26.15).
The project used existing datasets from previous LiDAR surveys to establish relationships and develop classification models to assess pavement marking retroreflectivity. The methodology obtains associations between field-measured pavement marking retroreflectivity and light intensity collected from LiDAR surveys. A scalable software solution was devised to identify and isolate pavement markings from the LiDAR point cloud, perform data filtering, and fit models using traditional regression models and machine learning to generate accurate assessments and classification of retroreflectivity levels.
RESULT
RESULT
This research shows that LiDAR point clouds can be used to extract quantitative, accurate estimates of retroreflectivity for pavement markings, providing a cost-effective, and reliable solution.
The new system allows UDOT to continually evaluate pavement marking retroreflectivity across the state as new LiDAR data becomes available. Since UDOT can assess the condition of its lane lines using data it already pays for, the need to perform separate expensive retroreflectivity surveys is drastically reduced. This approach saves 10's of thousands of dollars each year and provides the basis for new federal reporting required by September 1, 2026.
Most importantly, this new methodology enables UDOT to prioritize maintenance efforts effectively, ensuring roadway safety and optimal resource allocation.
LINKS
LINKS
Development and Validation of a Methodology to Estimate Retroreflectivity of Pavement Markings Using LiDAR - UT-26.15 (UDOT Research Report)
The findings from traditional studies (ground truth) were compared to LiDAR retroreflectivity assessments and proved to be almost 100% accurate for 1-mile segments
IMPACT
IMPACT
BETTER MOBILITY
BETTER MOBILITY
Bright pavement marking reduce traffic delays due to crashes
GOOD HEALTH
GOOD HEALTH
Drivers who can clearly see the lines on the road can better avoid crashes
Reducing the need for traditional surveys means employees are not on the road as much
CONNECTED COMMUNITIES
CONNECTED COMMUNITIES
N/A
STRONG ECONOMY
STRONG ECONOMY
Cost of retroreflectivity assessment is drastically reduced
EFFICIENCIES
EFFICIENCIES
Anticipated break-even by Sep, 2027 (1 year, 9 months)
Cost Avoidance: $1,224,209 over 20 years (Dec 1, 2025 - DecM1, 2045)*
Benefit/Cost Ratio: 9:1
*Cost and labor avoidance are the average benefits, net of initial and ongoing expenses, projected over the expected life of the innovation. See details.
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