Develop Efficient Prediction Model of Highway Friction on an Annual Basis on Texas Network

Project Details









Shelley Pridgen, Jorge Prozzi


Texas Department of Transportation


Data collection, Friction, Highways, Mathematical prediction, Skid resistance, Texture

Project description

The number of wet-weather crashes is a significant problem in Texas, consequently, the provision of pavement surfaces with adequate skid resistance or friction is of utmost importance for promoting public safety and saving lives. Measuring skid numbers for the entire Texas roadway network on an annual basis is challenging and inefficient because of the regular stops necessary to refill the water tanks. Fortunately, recent laser technology allows the measurement of texture at high resolution and speed in an efficient manner. Today, a contractor collects only macrotexture for TxDOT and delivers mean profile depth (MPD), which is a very poor predictor of skid. Consequently, TxDOT personnel have to go out and collect skid data at a high cost to calculate skid numbers. Currently TxDOT collects skid resistance on about 33% of their network on an annual basis (approximately 50% of the Interstate system and 25% of the non-Interstate system). The objective of this project is to (i) continue the work that started under TxDOT’s Project 0-7031, (ii) enhance the system that was developed as part of that project and (iii) update the models developed by collecting texture and skid on, at least 3,000 additional pavement sections distributed in at least six different districts. This information will be used to calibrate and validate equations to predict friction and skid numbers with a high degree of accuracy. This research would result in an enhanced system to collect texture data at highway speed for the entire Texas on-system network on an annual basis. The system is intended to be compact and capable of retrofitting to any surveying vehicle with minimal time and effort. This will provide not only savings but additional safety to operations.