# Prediction of Friction Coefficient Based on 3D Texture Characteristics of Road Surfaces
## 4 Conclusions  
This study investigated the prediction of road friction coefficients using 3D texture parameters of pavement surfaces. Measurements were conducted on 17 different rural road sections using the Traction Watcher One (TWO) device for friction coefficients and a newly developed Static Road Scanner (SRS) for capturing surface texture. After evaluating the correlations between individual texture parameters and the friction coefficient (\(\mu\)), multiple linear regression models were developed, incorporating and combining texture parameters.  
This study confirmed that both microtexture and macrotexture parameters are essential for accurate predictions, highlighting the importance of considering different texture scales. Analysis revealed that the strongest relationship between friction coefficient and individual texture parameters was achieved with the valley material portion \(S_{mr2,MIC}\), representing the surface microtexture. The mean curvature of the protrusions \(S_{pc,MAC}\) was identified as the most significant parameter describing macrotexture. The optimal combination of texture parameters for predicting the friction coefficient \(\mu\) was \(S_{mr2,MIC}\), \(S_{pc,MAC}\) and \(V_{vv,PS}\), achieving a coefficient of determination (\(R^{2}\)) of 0.8002 between measured and predicted \(\mu\) values. Using only \(S_{mr2,MIC}\) and \(S_{pc,MAC}\) resulted in a slightly lower \(R^{2}\) of 0.7809. However, at higher slip speeds, the significance of \(V_{vv,PS}\) is likely to increase due to its role in surface-drainage properties.  
Despite the strong overall performance, some variability and outliers were observed in the data. Future research should aim to understand and mitigate these discrepancies, possibly by incorporating more robust data-filtering techniques or exploring additional texture parameters that could account for these variations. Furthermore, integrating other advanced statistical methods or machine learning algorithms could potentially improve the prediction accuracy and robustness of the models developed.  
The results underscore the feasibility and accuracy of using non-contact texture measurements for reliable skid resistance prediction, which can significantly enhance pavement management and road safety. This approach offers a non-invasive method to monitor and predict road conditions, potentially reducing the need for more frequent physical inspections.  
Future work should focus on additional experiments to further enhance model precision and applicability. Specifically, including a wider range of friction coefficients (\(\mu\)) from 0.25 to 0.45, which were less represented in the current measurements, and conducting supplemental measurements on surfaces with \(\mu\) values higher than 0.75, would provide a more comprehensive dataset. Additionally, exploring the influence of higher-speed conditions would help to refine the model and its predictions.  
In conclusion, this study contributes valuable insights into the relationship between pavement texture and friction, paving the way for improved road safety measures and more effective pavement management strategies.  
Conceptualization, M.K.; Data curation, M.B. and P.P.; Formal analysis, M.D.; Funding acquisition, M.K. and M.D.; Investigation, M.K. and M.B.; Methodology, M.K.; Resources, M.K.; Software, M.B. and P.P.; Supervision, M.D.; Validation, M.K. and P.P.; Visualization, M.B.; Writing--original draft, M.B.; Writing--review and editing, M.K. All authors have read and agreed to the published version of the manuscript.  
This research was funded by VEGA grant number 1/0337/22. The APC was funded by VEGA 1/0337/22.  
Not applicable.  
The datasets presented in this article are not readily available because the data are part of an ongoing study.  
The paper is a result of the research supported by the research project VEGA 1/0337/22 \"Analysis of the influence of road surface texture on skid resistance, driving safety and the potential of particulate matter resuspension\".  
The authors declare no conflict of interest.