Monitoring and managing skid resistance properties are crucial activities to reduce the number of highway accidents and fatalities. However, current methodologies to measure pavement surface friction present several disadvantages that make them impractical. Thus, it is necessary to evaluate alternative methods to estimate friction. The principal objective of this study was to develop friction models based on pavement texture. We implemented a Line Laser Scanner (LLS) to obtain an improved characterization of the pavement texture which includes macrotexture and incorporates microtexture description using eight different parameters. Field measurements of friction and texture were collected around Texas using the British Pendulum Test (BPT), the Dynamic Friction Test (DFT), the micro-GripTester, and the LLS. The experimental results showed that there is not a unique relationship between texture and friction; though strong and statistically significant, the relationship is different for each type of pavement surface. Thus, regression analysis pooling all data cannot be utilized to quantify this relationship. For this reason, we applied a panel data analysis approach that allows the incorporation of the type of surface and provides a more robust analysis. The results indicate that the prediction of friction is significantly improved when incorporating information from both macrotexture and microtexture into the prediction model. Therefore, a measure of microtexture should be included into friction models based on texture. In addition, the study of different texture parameters suggests that the mean profile depth (MPD) is the most significant parameter for macrotexture and for microtexture to explain the distinct friction measures.

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Proper tire–pavement interaction is essential for the safety of motorists. Pavement surface texture is a major contributing factor to tire–pavement friction. This study performed a series of statistical analyses of field-measured friction and texture data to find the texture–friction correlation. Three test sections with different pavement types were selected within the state of Texas. Data were collected at three locations in the right wheel path and three locations in the center of the lane for each test section. To measure the texture data, the researchers used the circular track meter (CTM) and a prototype measurement device developed in-house and consisting of a line laser scanner (LLS). Friction measurements were obtained with the dynamic friction tester (DFT) and Grip-Tester. The mean profile depth (MPD) was calculated by using the measured texture data. The relationship between the MPD values and the friction numbers obtained from the Grip-Tester and DFT was investigated at speeds of 50 and 70 km/h (31.1 and 43.5 mph). The repeatability and reliability of both the developed LLS prototype and the Grip-Tester were also evaluated, as well as the effect of test speed on friction measurement. The results indicated a strong positive correlation between the texture and friction data. In addition, the developed LLS prototype was able to scan the pavement surface texture more reliably and precisely than the CTM in terms of vertical and horizontal resolution. The Grip-Tester showed promising results compared with the DFT with regards to the friction measurement.

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