Project Details

Abstract

Pavement marking retroreflectivity plays a significant role in communicating drivers their location with respect to roadway lanes, particularly during low light and low visibility conditions. As such, retroreflectivity plays important role in traffic operations and safety. Moreover, a recent ruling from FHWA introduced a mandate for transportation agencies to maintain minimum levels of retroreflectivity on public roads, resulting in the need for agencies to measure and monitor pavement markings in order to maintain compliance with such requirements. Typically, retroreflectivity of pavement markings is measured using retroreflectometers operated manually or mounted on mobile devices. However, even though mobile setups allow for the collection of measurements at driving speeds, they require special equipment and represent significant costs. Therefore, improvements leading to retroreflectivity estimates without extensive data collection needs could represent a significant reduction in related costs and data processing. The proposed project targets an opportunity to achieve such benefits, by repurposing LiDAR datasets the agency already collects periodically, and postprocessing them to extract retroreflectivity estimates intended to supplement and eventually replace field measurements using reflectometers.

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