## 2 Materials
### Data Extraction  
Relevant studies have taken pavement structure, climate and traffic as influencing factors to study pavement roughness, as shown in Figure 2. The data used in this study were obtained from the LTPP database, which is publicly available through the LTPP office website at [[https://infopave.fhwa.dot.gov/Data/DataSelection](https://infopave.fhwa.dot.gov/Data/DataSelection)]([https://infopave.fhwa.dot.gov/Data/DataSelection](https://infopave.fhwa.dot.gov/Data/DataSelection)), (accessed on 12 February 2024) [33]. Using the ACCESS tool (Microsoft Access 16.0), this study constructed a spatial-temporally aligned multi-source dataset for pavement roughness. The selection of influencing factors for this study are shown as follows:  
1. [label=(0)]
2. Pavement structure properties. Variables including total asphalt, thickness and total pavement thickness have been well recognized as critical factors affecting the pavement performance [34]. Structural number, layer thickness and subgrade modulus are adopted to represent the pavement structure properties [35]. Therefore, this paper introduces the pavement material type and thickness as input variables. The refined pavement structure factors enable the model to fully consider the effect of material differences on pavement roughness.
3. Traffic volume, climate and service age have significant impacts on performance [36]. The number of actions of the equivalent standard axle load (ESAL) is used to represent the traffic loads. This variable is able to quantify the cumulative effect of traffic loads on pavement damage more accurately, capturing the pavement roughness degradation due to traffic loads.
4. Climatic conditions. Climate is considered in prediction models in [24; 37]. This study not only includes conventional indicators such as temperature, humidity and precipitation but also considers the number of freeze-thaw cycles as well as shortwave radiation. The complex effects of climatic conditions on pavement roughness are systematically analyzed through a multi-dimensional combination of climatic factors.  
Figure 1: Quarter car model.  
Performance data. This study extracts pavement performance data corresponding to the latest IRI in the LTPP database. This study does not incorporate temporal continuity into the prediction model because the available IRI data are collected on an annual basis rather than shorter, continuous time intervals. Instead, the average of the left and right wheel path IRI values (mean roughness index) over the last five years was used as the IRI value. This approach provides a stable representation of pavement performance while mitigating the impact of single-year anomalies.  
This study initially selected 14 independent variables for predicting IRI. The dependent variable was the average IRI value of the left and right wheel tracks. Table 1 presents the details of these variables. The 'Source LTPP data sheet' in Table 1 provides the relevant table names corresponding to the variables in the dataset. To replicate the data extraction process, users can navigate to the Standard Data Release 38 (SDR38) and follow the table names to extract pavement information. A detailed description of the variables listed in Table 1, including the ranges of continuous variables and the numerical encoding of categorical variables, is provided in Tables A1 and A2 in Appendix A.