Texture data is an important measure of a pavement’s surface, which ultimately affects the safety of every commuter. The resulting statistic, Mean Profile Depth (MPD), is useful in the prediction of the high speed dependence of wet pavement friction. The Federal Highway Administration (FHWA) Long-Term Pavement Performance (LTPP) program is the largest pavement performance research program ever undertaken, gathering data from 2,400 pavement test sections for over 25 years. Since 2013, LTPP has collected macrotexture measurements from sections located on in-service highways throughout North America. Profilers, meeting LTPP’s stringent acceptance criteria, use texture height sensors, rated at 62.5 kHz, to collect texture measurements at 0.5 mm intervals. Texture statistics, including MPD, RMS, Dropouts, and Cubic Skew populate the world’s largest pavement performance database (PPDB).
This paper presents an investigation of texture statistics across various pavement types, materials, maintenance, rehabilitation, traffic load levels and climatic zones. This paper presents quantification of correlations between sections in the experimental matrix and provides an update on the existing texture statistic ranges. This in turn, can result in a greater understanding of the role that texture plays in ensuring the safest highways possible.
