## 3 Methods for the Characterisation and Evaluation of the Skid Resistance of Asphalt Pavements
### Asphalt Pavement Skid Resistance Evaluation Model  
Traditional pavement macro- and microtexture tests are conducted under controlled conditions; however, the type of equipment, test speed, test temperature, and humidity all influence the test results during actual pavement skid resistance testing [81; 82]. To accurately evaluate pavement skid resistance, researchers have established various models to describe the variability in skid resistance by integrating the effects of environmental factors on test results [83; 84]. The most commonly used models include the Empirical Formula Model, the Penn State Model, the Modified Penn State Model, the Asymptotic Model, the PIARC Model, the International Friction Index Model [22], the Pavement Skid Resistance Prediction Model, and the Fractal Difference Function Model, as shown in Table 3.  
The Empirical Formula Model is as follows: (1) \(\mathrm{Y}=\mathrm{AInx}+\mathrm{B}\) (2) \(\mathrm{Y}=\mathrm{A}\) e B x. [PERSON] et al. [85] analysed the change rule of pavement skid resistance using these two models. The results demonstrated that these two models were unable to simulate the process of pavement skid resistance attenuation caused by the pre-compaction stage. Nevertheless, the logarithmic curve model is capable of accurately representing the stability stage in the later phase of asphalt pavement skid resistance decay. The Penn State Model evaluates the skid resistance of pavements by investigating the relationship between the pavement friction factor F and the test wheel slip velocity S. In this case, the slip speed S is directly related to the macroscopic texture of the pavement, and S can be classified into three cases according to the test equipment: 1. Locking wheel friction coefficient metre: S = V; V is the speed of the test vehicle. The same as below. 2 Fixed slip rate friction coefficient metre: S = V \(-\) K; K is the slip rate. 3. Lateral force type friction coefficient metre: S = V \(-\) sin\(\alpha\); \(\alpha\) is the deflection angle of the deflection wheel. Due to the low test speed of the friction factor measuring instrument at this stage and despite the fact that the speed during the test is not 0, the friction factor of the test equipment at low speeds can only be obtained by calculation. The smaller values of S\({}_{0}\) indicate that the friction factor of the road surface is high at lower slip velocities. Conversely, the larger values of S\({}_{0}\) imply that the velocity has a small effect on the friction factor [86]. [PERSON] et al. [87] used the friction factor calculated at a slip velocity of 10 km/h instead of the friction factor F\({}_{0}\) at a slip velocity of 0 and F\({}_{10}\) = 10 km/h to obtain the modified Penn State model. The modified Penn State Model is only capable of evaluating the relationship between friction factor and speed at a speed of 10 km/h. This model is not applicable to the full range of test equipment due to the instrument's test speed varying widely and being unable to be controlled stably at 10 km/h. The Asymptotic Model is able to predict the asphalt pavement pendulum value and the construction depth, respectively. When x = 0, y = A + C represents the initial skid resistance value of the asphalt pavement. When x\(\rightarrow\)\(\infty\), y = C denotes the final value of the pavement after the decay of skid resistance [88]. [PERSON] et al. [89; 90] conducted accelerated loading tests on AC-13, SMA-13, and OGFC-13 mix pavements using the Asymptotic Model. They concluded that the predicted results of the Asymptotic Model were closely related to the number of vehicle axle loads.