This article deals with the possibility of predicting skid resistance based on non-contact scanning of the road surface. The study is based on comparing pavement texture parameters with coefficients of friction measured on a wide variety of road surfaces, while other test conditions were the same and constant. The measurements of the coefficient of friction were performed using a pendulum tester. The pavement texture was measured using a static road scanner, and 85 different 3D texture parameters were calculated. The study shows that the determination of the friction using only single texture parameters is not sufficient. Based on this statement, the next part of the research analyzed the influence of the mutual combination of surface texture parameters. A linear regression model was chosen to determine the friction coefficient prediction formula based on the combination of texture parameters. Statistically, the most significant parameters in the prediction model proved to be the valley material portion, characterizing the microtexture, and the arithmetic mean curvature, characterizing the pavement macrotexture. The obtained regression model proved to be statistically significant with R² = 0.81 for Pendulum Test Value prediction.

Accurate assessment of road pavement friction is crucial for maintaining road safety. This study explores the prediction of the friction coefficient (µ) using 3D texture parameters of pavement surfaces. Measurements were conducted on 17 different rural road sections using the Traction Watcher One (TWO) for friction coefficients and a newly developed Static Road Scanner (SRS) for surface texture. Multiple linear regression models were created, incorporating texture parameters such as the valley material portion (S_mr2,MIC), arithmetic mean peak curvature (S_pc,MAC), and dale void volume (V_vv,PS). The results demonstrate a strong correlation between texture characteristics and friction, with R² values up to 0.80 and an RMSE as low as 0.076, validating the model’s accuracy. This approach highlights the potential of using non-contact texture measurements for reliable prediction of friction, offering a significant advancement in pavement management and safety.

Road transport is a source of exhaust and non-exhaust emissions of particulate matter (PM). Non-exhaust PM emissions include road surface wear, tires, brakes and road dust resuspension. An important part of PM in urban air consists of particles that originate from the resuspension of road dust. This study focused on the analysis of the physicochemical properties of road dust and PM₁₀, PM_2.5 and PM₁ (PM_x) in the air (size, concentration, distribution, content of chemical elements), the properties of urban aerosol (number, mass and area distribution), and at the same time, the interconnection between the detected chemical elements in road dust and individual PM fractions in the air in order to reveal the sources of PM in the Žilina City, Slovakia. The presence of various chemical elements was found in road dust, of which the highest concentrations (more than 100 mg/g) were the elements Ca, Si and Al (specifically 373.3 mg/g, 351.4 mg/g and 113.9 mg/g on average from four sampling sites). The concentrations of PM₁₀, PM_2.5 and PM₁ were, on average, 27.2 μg/m³, 19.5 μg/m³ and 14.5 μg/m³ during the measurement period according to the reference gravimetric method. The chemical elements K, S, Cd, Sb, Pb, Ni and Zn were detected and the most represented (more than 60%) in the fine PM_2.5 fraction, and the chemical elements Mg, Al, Si, Ca, Cr, Cu, Fe and Ba were the most represented in the coarse PM_2.5–10 fraction. The analysis of the aerosol in the range of 12 nm–20 μm revealed a bimodal distribution of the collected sample of the investigated urban aerosol. This study provides a comprehensive view of the properties of road dust, airborne PM and aerosol (up to the size of nanoparticles), which can contribute to the expansion of knowledge in this field.

In this study, the traffic noise degradation in asphalt pavements was analysed using the ‘Statistical Pass-By method’. The sound levels of two surfaces were monitored during 9 and 12 years of service, respectively. By comparing the dependencies of the maximum A-weighted sound pressure level on logarithm of vehicle velocity, an increase in the sound level was found at all recorded speeds. Following an analysis of sound levels, as combined with the statistical pass-by index (SPBI) calculated versus age (expressed in vehicles), it was determined that the noise is an increasing power function of SPBI values on vehicle passes, based on an approximation of noise level adjustment to a reference temperature of 20 °C (using a coefficient of 0.06 for asphalt concrete surface AC11 and - 0.03 for mastic asphalt SMA11). The adjusted traffic noise degradation model showed that the SMA11 surface has a higher resistance to acoustic degradation than AC11 surface.

This article’s primary goal was to analyze the effect of texture on skid resistance. Surface texture was recorded with a revolutionary device designed to create 3D surface scans, the Static Road Scanner. The skid resistance was represented by a pendulum test value. Measurements were made on three different groups of surfaces. Reference surfaces with known standard grain sizes represented the first group. The second group consisted of specimens made from a different type of aggregate. The last group of surfaces consisted of asphalt specimens made from different sizes and types of aggregates used in a mixture. The test results shed some more light on understanding texture’s effect on surface friction. Although some results were expected, not all of them were proven. For instance, a high level of texture doesn’t necessarily mean high friction. A relatively strong relationship was found between friction and microtexture on the reference surfaces with grain sizes up to 125 µm. However, the relationships between texture and skid resistance on the aggregate and asphalt specimens turned out to be shallow for the investigated samples. For this reason, it was recommended to expand the number of investigated surfaces in further research to ensure sufficiently different levels of texture.

Road pavement roughness, in terms of skid resistance, can be described from a geometrical point of view as a texture or from a physical point of view as friction between a tire and a road surface. The paper deals with the comparison of asphalt and concrete pavement surface on selected newly built sections of the D1 motorway near the Ovčiarsko tunnel. Texture measurements were performed with a Static Road Scanner (SRS) capable of recording surface irregularities up to the microtexture level (2.49 µm resolution). A pendulum was used to determine the friction. Subsequently, the texture was evaluated using individual amplitude and wavelength characteristics and the friction was evaluated using the PTV parameter. Finally, correlations were searched between the roughness characteristics of asphalt concrete and cement concrete pavements, but also between texture and friction characteristics.