## 1 Introduction  
Road traffic accidents closely relate to driver, traffic, weather and road conditions [1, 2]. According to statistics, traffic accidents caused by insufficient pavement skid resistance account for about 10% of the total [3], while improving the level of surface textures and pavement friction has a clear benefit on the reduction of accident rates [4]. However, under the combined action of traffic loads and adverse weather conditions, the pavement skid resistance performance degrades over time. Therefore, the prediction of long-term asphalt pavement skid resistance is essential for road safety. In order to study the surface textures and pavement friction degradation of asphalt pavement, full-scale instrumented track tests have been carried out in a few countries. Among them, the American AASHTO test road has been tested since 1958. After two years of real traffic loading and the application of total 1.1 million axle loads, the friction coefficients decreased from 0.72 and 0.63 to 0.44 and 0.22, respectively, for the flexible pavement and the rigid pavement [5]. Based on the National Center for Asphalt Technology (NCAT) test track constructed in 2000, the friction coefficient and the skid number on several road sections were measured using a dynamic friction tester and a locking wheel anti-skid trailer [6]. Subsequently, a surface shot peening treatment was proposed to improve the long-term skid resistance of the asphalt pavement [7]. The Minnesota Department of Transportation began construction of MnROAD in 1991 and opened the full-scale pavement research facility to live traffic in 1994. The skid number on asphalt pavement road sections was measured using the lock wheel anti-skid trailer, and a relationship among the skid numbers, service time, and loading conditions was established [8]. In China, RIOHTrack was built in November, 2015, aiming at verifying the design and construction of long-life pavement. Based on the RIOHTrack, [PERSON] et al. (2020) carried out a correlation analysis of British pendulum number (BPN) test value and mean texture depth measured by te sand patch method (MTD) test value. They found that there is a linear growth law between the two indicators, and the correlation coefficient reaches 0.99 [9]. [PERSON] et al. (2018) reported the variation of sideways force coefficient (SFC) and sensor measured texture depth using a laser method (SMTD) measured on RIOHTrack during 2017, but they did not establish a prediction model to characterize these indicators [10]. [PERSON] (2019) proposed exponential and logarithmic functions with regard to the short-term prediction of SFC by considering the influencing factors of load level and loading cycle numbers [11]. Up to now, the research based on full-scale accelerated pavement testing has mainly focused on the comparison of friction among different road sections and the degradation trend of skid resistance. There is no surface texture model that considers the aggregate factors based on the aggregate gradation parameters from the Bailey method. The main factors considered in surface texture modeling need to be further improved. Generally, the existing surface texture models for asphalt pavement take into account the influences from the traffic volume (the cumulative standard axle loads) [12]. The Bailey method provides a promising approach for the design of asphalt mixtures and the evaluation of aggregate packing and voids. It can realize the optimal design of a dense skeleton interlocking anti-skid wearing course and improve the pavement surface textures and the skid resistance [13]. Therefore, the aggregate parameters considered in the Bailey method can be taken as the important influencing factors to predict the sensor measured texture depth in the surface texture model, namely, the coarse aggregate ratio (CA), the fine aggregate coarse ratio (FAC), and the fine aggregate fine ratio (FAF). In this study, a random-effects panel data-based prediction model is developed to evaluate the asphalt pavement surface texture in terms of SMTD.