# A Review of Long-Term Skid Resistance of Asphalt Pavement
## 5 Conclusions  
This paper reviews and summarizes research hotspots, such as the indoor and outdoor skid resistance testing methods of AP, the skid resistance mechanism of AP, and the L-TSR decay law of AP, influencing factors, decay model, and predictive model. The main conclusions are as follows:  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Prediction Models** & **Influencing Factors** & **Advantages** & **Disadvantages** \\ \hline BP neural network model & & Characterized by strong self-learning ability and handling of uncertainties, it can establish a highly nonlinear relationship between complex factors and pavement skid resistance, which is more applicable to the prediction of pavement skid resistance & Localized optimization and inefficient learning \\ BP neural network model & Number of nodes in layer, number of nodes in hidden layer, sample allowable training error & Compared to a single neural network model or genetic algorithm, a combined prediction model can greatly improve prediction accuracy & The operation efficiency is not as good as that of a single neural network model \\ Genetic algorithm model & Parameter settings, evaluation functions, input genes, runtime & Genetic algorithms are globally searchable, adaptable, and robust & Operational inefficiency, complex programming, and empirical setting of parameters such as crossover rate and mutation rate \\ \hline \hline \end{tabular}
\end{table}
Table 6: _Cont._(1) The digital image method can quickly and intuitively obtain information such as pavement texture. By analyzing the image data, the skid resistance of the pavement can be accurately evaluated. The intelligent sensor detection method can monitor the parameters related to the skid resistance of the pavement in real time and continuously provide timely feedback on the changes in the pavement condition and ensure the traffic safety of the road.  
(2) The indoor test can effectively evaluate the performance of skid resistance materials under different environmental conditions by simulating the actual road conditions, which can help to evaluate the effect of anti-skid materials and the performance of AP more accurately and to verify the reliability and repeatability of the results, as well as to provide detailed data on the performance of the materials, including the skid resistance coefficient of friction and so on.  
(3) The coefficient of friction for skid resistance is provided by the adhesion component and deformation component. Improving asphalt viscosity, adopting aggregates with high-quality aggregates, ensuring the content of fine aggregates on the surface (0.16-4.75) mm, and controlling the surface layer slurry can ensure that AP has sufficient skid resistance and can improve the durability of the pavements. The preparation of AP by incorporating rubber, steel slag, slag, or based on the principle of differential honing, which reduces the economic outlay, saves resources in terms of high-quality aggregates and improves the skid resistance of AP.  
(4) For the L-TSR of AP, the local pavement conditions, climatic conditions, and environmental factors should be considered. 1 The mineral composition of rich matrix hardness difference between the aggregate should be selected; 2 a high-quality asphalt and asphalt dosage control should be selected; 3 a reasonable mineral grading should be chosen; 4 the vehicle load and driving speed limit signs should be set up, and from time to time on the AP, the asphalt should be cleaned up; and 5 high-quality modified asphalt should be used to improve the L-TSR of AP.  
(5) The combined model synthesizes the advantages of various models and makes up for the shortcomings of a single model, which is the best choice for predicting the L-TSR of AP. In addition, the application of the combined model is not limited to the traditional prediction and analysis field but can also be extended to many fields such as image processing and machine learning.