Abstract
Road surface type is a key input to most asset management and maintenance management models and analyses, such as deterioration prediction models, life cycle cost analysis, and need estimates. However, surface type changes in space and time due to the use of varying pavement types and the application of different surface treatments. In recent years, the transportation and municipal industry has begun to use mobile lidar (Light Detection and Ranging) systems to collect roadway condition and inventory data, including surface type. This paper provides a heuristics-based method for detecting road surface type based on statistical analysis of laser reflected signal intensity. The studied surfaces are open graded asphalt, dense graded asphalt, seal coated asphalt, concrete, and roadside vegetation. This method will improve the availability and quality of surface type data, especially for large roadway networks, by automating the process of obtaining this information from mobile lidar measurements.
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