## 1 Introduction  
Understanding and evaluating pavement performance conditions are the basis for pavement performance prediction [1; 2; 3; 4]. Accurate pavement performance inspection data and reliable pavement performance predictions provide better insight into when and where repairs or maintenance are required [5; 6; 7; 8]. This extends the service life of the structure and effectively increases the cost-effectiveness of road construction and maintenance [9; 10; 11].  
With the rapid development of highway infrastructure, the research on pavement performance evaluation methods is also changing and deepening [12; 13; 14; 15; 16]. At present, in engineering applications, the evaluation methods of pavement mainly include average value and discrete grade evaluation. The former is to obtain the average value of the observed field pavement condition data of the entire route. The latter is to perform statistical analysis on the evaluation results of the pavement performance of the unit road section. Determine the proportions of excellent, good, medium, poor, and poor highways. This method has also been applied in road network evaluation to determine the proportion, length, and distribution of highways in the road network of excellent, good, medium, poor and poor grades [17]. However, in the evaluation method based on the average value, the pavement conditions within 1 km have been compressed when evaluating a road section using a 1 km unit road section. Then, the average value of all road sections is taken as the evaluation result of the road performance of the whole route. In this process, the information carried by the data is compressed and ignored again. When making predictions based on the mean value, 50% of the actual conditions may be better or worse than the predicted one if the symmetric dispersion is based on the mean value of the indicators [18].  
The process of converting continuous unit-kilometer-level indicators into discrete pavement levels is also a cover-up of detailed information on pavement performance. For example, if the performance index score of some sections of a route is 80.1, and the score of another route is 79.9, then the latter will be rated as \"medium\", and the former is still rated as \"good\". While the latter section with a lower road performance level attracts attention, it is easy to ignore the former.  
Using a distribution [19; 20; 21], on the one hand, does not average the underlying unit-kilometer-level metrics. On the other hand, distribution avoids the loss of information due to averaging and discretization of evaluation results. It maximizes the retention and utilization of all unit-kilometer-level indicator data.  
However, when using probability distribution to deal with pavement variability, different variables have different distribution forms, and a unified equation form cannot be adopted [22; 23; 24; 25]. For example, [PERSON] et al. found that Burr distribution is the best distribution for pavement cracking and permanent deformation fitting [19]. [PERSON] et al. used Gamma distribution to fit loose pavement and exponential distribution to fit pavement potholes [26].  
Therefore, this paper introduces the sampling theorem and uses the interpolation function and the unified equation to evaluate the overall level, balance, concentration, and dispersion range of the road network and road section pavement performance. To this end, this paper introduces the sampling theorem. Using interpolation functions and unified equations to evaluate the overall level, balance, concentration, and dispersion range of pavement performance.