## 5 Conclusions  
To investigate the degradation tendency of asphalt pavement surface texture, four years of SMTD panel data (characterized by sensor measured texture depth) measured from 19 pavement structures including 4 types of surface asphalt layer were used to develop a surface texture prediction model. For the first time, the Bailey method-based aggregate parameters were considered as the influencing factors in surface texture prediction model. The significant levels of 7 selected influencing factors were determined through a series of panel data analyses. This study proves the feasibility and effectiveness of the panel data analysis method in the evaluation and prediction of the surface texture of a full-scale accelerated pavement testing loop. The results of this study can benefit the material design, construction, maintenance treatment of asphalt pavement, and the evaluation of asphalt pavement performance for better long-term service. The main conclusions are as follows:  
1. The surface texture measured by a laser method (SMTD)-based prediction model for asphalt pavement has been developed using a panel data analysis method. The proposed prediction model takes into account the dependence on time scale of input panel data, diversified road sections, traffic factors, environmental factors, and the Bailey method-based aggregate gradation parameters. Compared with the mixed-effects and the fixed-effects models, the random-effects model is the most suitable form to predict the asphalt pavement surface texture. The random-effects model has good regression effect and prediction accuracy for SMTD. 2. The impacts of the time scale of input panel data on the significance level of the 7 selected influencing factors for surface texture prediction were investigated. The cumulative standard axle loads (CSAL), monthly average humidity (MAH), CA, FAC, and FAF have significant effects on pavement surface texture prediction, while the significance level of the monthly average temperature (MAT) and monthly rainfall (MR) changes with the extension of time scales of the input data. The cumulative standard axle loads (CSAL), monthly average humidity (MAH), monthly rainfall (MR), CA, and FAF are negatively correlated with SMTD. The monthly average temperature (MAT) and FAC ratio are positively correlated with SMTD. 3. The regression and prediction ability of the surface texture prediction model based on different time scales of SMTD panel data were evaluated. The prediction model using one year SMTD panel data (year 2017) shows the fastest decrease trend in the long-term (year 2021-2026), whereas the prediction model based on four years SMTD panel data (from year 2017 to 2020) presents the slowest decreasing tendency. The extension of the time scale of input panel data can ensure long-term prediction robustness for the panel data analysis-based surface texture prediction model. It should be noted that the model developed in this work was established and calibrated using the RIOHTrack data; to date, there has not been a large amount of field SMTD test data on asphalt pavements in China or worldwide for developing prediction models. Conceptualization, W.H. and Y.W. ; Data curation, C.L. and W.G. ; Writing--original draft, C.L. ; Writing--review & editing, W.H. and Y.W. ; Funding acquisition, Y.W. All authors have read and agreed to the published version of the manuscript. This research was funded by National Key R&D Program of China under grant number 2021 YFB3202902. Not applicable. **Data Availability Statement:** 3 rd Party Data Restrictions apply to the availability of these data. Data was obtained from Research Institute of Highway, Ministry of Transport of China and are available from [[https://www.roadsdata.cn/pages/shujufuwu/index.html](https://www.roadsdata.cn/pages/shujufuwu/index.html)]([https://www.roadsdata.cn/pages/shujufuwu/index.html](https://www.roadsdata.cn/pages/shujufuwu/index.html)) with the permission of Research Institute of Highway, Ministry of Transport of China.