## 3 Generation of Surface Texture Panel Data  
### Introduction of Panel Data Analysis  
Panel data is a two-dimensional data set that contains time series data and cross section data [24]. A dataset containing \(T\) observation periods and \(N\) objects is denoted as \(y_{it}\). The subscript \(i\) denotes the number of objects (\(N\)), and the subscript \(t\) denotes the number of observation periods (\(T\)). Accordingly, the dataset of the \(i\)-th object across \(T\) observation periods is expressed as \(\{y_{it}\}_{t=1}^{T}\), which is called the time series panel data in the \(i\)-th longitudinal cross section. The dataset of \(N\) objects across the \(t\)-th observation period is expressed by \(\{y_{it}\}_{i=1}^{N}\), which is called the transversal section panel data in the \(t\)-th observation period. Currently, the panel data analysis method is less used for the characterization of pavement performance. Theoretically, this method is of benefit in understanding and predicting the short and long-term surface texture of asphalt pavement, since it extends the sample data and can provide more accurate and effective estimates. Using the panel data analysis method, three basic types of models can be established, i.e., mixed-effects (ME), fixed-effects (FE), and random-effects (RE) models [25]. [PERSON] [26] developed a random-effects panel data model to assess the international roughness index (IRI) of asphalt pavement. The model obtained a good result for the key factors affecting asphalt pavement IRI. [PERSON] [27] proposed a mixed-effects IRI prediction model for asphalt pavement. Compared with an earlier developed time series model, this mixed-effects panel data model has a higher precision in the long-term prediction. Taking the skid number as the indicator, [PERSON] [28] developed a random-effects panel data model to predict the skid resistance of asphalt pavement. The regression between the predicted and the measured results was fitted with the R\({}^{2}\) coefficient of 0.74. Based on the LTPP test road, [PERSON] [29] developed a fixed-effects panel data model using the skid number as the indicator of skid resistance. A satisfactory fitting performance was provided by this model since the R\({}^{2}\) reached 0.94. Therefore, the performance prediction model of asphalt pavement developed by the panel data analysis is satisfactory for the pavement roughness prediction using IRI as the indicator and the skid resistance prediction using skid number as the indicator. The sensor measured texture depth data collected from full-scale asphalt pavement track is a typical kind of panel data, which contains two dimensions, i.e., the time series data and the cross section data. Compared with single time series data, the panel dataset contains dynamic performance variations in different asphalt pavement sections. The surface texture panel data of RIOHTrack contains the time series data from January 2017 to December 2020, and the cross section series data of 19 pavement structures (Table 3). For a certain month, the panel data is the cross section data containing 19 road sections; as for a certain road section, the panel data is the time series data of this road section across 48 months. The panel data processed in this study is composed of 19 individuals and 48 months. Accordingly, the panel data of each unknown influencing factor contains 19 individuals and 48 months; thus, a total of 912 observations should be considered.