## 3 Generation of Surface Texture Panel Data
### Aggregate Factors Affecting Surface Texture  
[PERSON] and [PERSON] (2017) defined the aggregate hardness parameter (AHP) as the sum of average hardness (DMP) and contrast of hardness (CD), and indicated a positive correlation between AHP and the friction coefficient of asphalt pavement [30]. For now, few researchers try to introduce the Bailey method-based aggregate parameters (CA, FAC, and FAF) as the basic factors in the prediction model of surface texture. In this paper, the Bailey method-based aggregate parameters, for the first time, are considered as the influencing factors in the surface texture prediction model.  
The Bailey method controls the passing percentages of key sieve size of coarse and fine aggregates through CA, FAC and FAF parameters, so that asphalt mixture can form a dense anti-sliding wear layer with embedded structure. Therefore, the aggregate parameters based on the Bailey method are closely related to the skid resistance [12; 30]. Unlike the conventional design methods, the Bailey method uses a different definition of coarse and fine aggregate, taking the sieve size corresponding to 0.22 times of the nominal maximum aggregate size (NMAS) as the critical size that distinguishes the coarse aggregate from the fine aggregate.  
The Bailey aggregate gradation parameters include CA, FAC, and FAF ratios [13]. The CA ratio defines the shape of the coarse aggregate portion of the gradation. If the CA ratio is too large, the coarse aggregate skeleton of asphalt mixture cannot be formed; if it is too  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Indicator** & **Influencing Factors** & **Abbreviation** & **Description** \\ \hline \multirow{5}{*}{sensor measured texture depth by laser method (SMTD)} & traffic & CSAL & cumulative standard axle loads \\ \cline{2-4}  & & MAT & monthly average temperature (\({}^{\circ}\)C) \\ \cline{2-4}  & environment & MAH & monthly average humidity (\%) \\ \cline{2-4}  & & MR & monthly rainfall (mm) \\ \cline{2-4}  & & CA & coarse aggregate ratio (\%) \\ \cline{2-4}  & aggregate & FAC & fine aggregate coarse ratio (\%) \\ \cline{2-4}  & & FAF & fine aggregate fine ratio (\%) \\ \hline \hline \end{tabular} Note: CSAL corresponds to the cumulative standard axle loads in the same month of the measurement, MAT corresponds to the average temperature in the same month of the measurement, MAH corresponds to the average humidity in the same month of the measurement, MR corresponds to the total rainfall in the same month of the measurement.  
\end{table}
Table 3: Summary of influencing factors of surface texture panel data model.  
small, the problems of segregation and insufficient compactness may occur. The FAC ratio describes the interlocking and filling effects of the coarser portion in fine aggregate, and the FAF ratio describes the filling compactness of the finer portion in fine aggregate. The calculation formulas are as follows:  
\[\text{PCS}=0.22\times\text{NMPS} \tag{6}\]  
\[\text{CA}=\frac{\text{P}_{\text{NMPS/2}}-\text{P}_{\text{PCS}}}{\text{P}_{100 \%}-\text{P}_{\text{PCS}}} \tag{7}\]  
\[\text{SCS}=0.22\times\text{PCS} \tag{8}\]  
\[\text{FAC}=\frac{\text{P}_{\text{SCS}}}{\text{P}_{\text{PCS}}} \tag{9}\]  
\[\text{TCS}=0.22\times\text{SCS} \tag{10}\]  
\[\text{FAF}=\frac{\text{P}_{\text{TCS}}}{\text{P}_{\text{SCS}}} \tag{11}\]  
where, CA is the coarse aggregate ratio, FAC is the fine aggregate coarse ratio, FAF is the fine aggregate fine ratio, NMPS is the nominal maximum particle size, P\({}_{\text{NMPS/2}}\) is the passing percentage of the sieve corresponding to 1/2 nominal maximum particle size, PCS is the key controlling sieve, P\({}_{\text{PCS}}\) is the passing percentage of PCS, P\({}_{100\%}\) is the passing percentage of the maximum sieve, SCS is the first controlling sieve of fine aggregate, TCS is the second controlling sieve for fine aggregate, P\({}_{\text{SCS}}\) is the passing percentage of SCS, and P\({}_{\text{TCS}}\) is the passing percentage of TCS.