## 2 Literature Review  
### Research on Predictive Model Development Using Percent Performance Database  
Road pavement is one of the most easily and frequently encountered infrastructures in modern society, and the budget for managing its vast extension at a satisfactory level for users has always been insufficient. As one of the efforts for more effective and long-term systematic management, several revisions have been made to secure and predict pavement performance since the 1959 American Association of State Highway Officials (AASHO) road test in the United States.  
Several Departments of Transportation in the United States have attempted to identify the functional and structural status of road pavements and help with the decision-making of maintenance at the network level through the pavement condition rating (PCR) index, developed based on a mechanistic-empirical design in the 1970s-1980s [4]. PCR was constructed using time series data collected over two years, and the R\({}^{2}\) value of the final regression equation was derived in the range of 0.70-0.76. As a result of managing road pavement in the United States using PCR, a slow deterioration rate was observed in the initial life section, suggesting that the deterioration rate would increase rapidly after 15 years, resulting in higher maintenance costs.  
As the use of PCR began in earnest, the importance of a performance prediction model increased in PMS, and it started being considered essential for technical and economic management at the network and project levels. In particular, carefully determining the optimal decision-making and management strategy application timing was necessary as the prediction results for road pavement performance may lead to astronomical costs. As a result of more detailed classification of factors affecting the deterioration of the pavement condition index compared to PCR, non-linear regression modeling could be performed using the distress index, structural index, and roughness index as variables [5]. In particular, an index that considered both the new and rehabilitated pavements was developed.  
In the 2000s, along with the rapid development of computing technology and the Internet of Things, especially the development of road pavement condition survey equipment linked with the geographic information system (GIS) and the global positioning system (GPS), predictive models were developed using more extensive and complex data research. With increased computing speed, predictive models that could consider several cases began to be developed. Among them, Markovian prediction was one of the methods employed to predict the degree of deterioration of the pavement that could be used for decision-making on future management or rehabilitation [6]. In addition, GIS and GPS have been linked to the existing ARAN equipment, and future pavement management methods using imaging, scanning, and automatic interpretation technologies and the development direction of the mobile mapping system (MMS) have been suggested [7].  
Since approximately 10 years ago, various studies using a sizable database covering all factors affecting pavement other than road pavement have been conducted for more systematic management of the ever-growing amount of pavement. In addition to distress, roughness, and structural number (SN), which are representative variables used for road pavement management thus far, the amount of change in the pavement condition index has been analyzed by introducing the concept of change amount for each element [8]. In addition to the management aspect of road pavement, management measures, including road user safety, have been proposed [9], and skid variables other than distress, roughness, and SN have been added. Furthermore, unlike the generally applied regression modeling, the condition index was created by linking the analytical hierarchy process, an expert questionnaire method, and regression modeling based on the road photos and analysis results collected through the MMS equipment [10].