###### Abstract
Artificial neural networks (ANNs), as a representative of deep learning (DL), are a common tool for building pavement performance prediction models of flexible and rigid pavements [25; 26]. Based on the data extracted from the long-term pavement performance (LTPP) database, [PERSON] et al. [27] used an ANN-driven forward sequential feature selection algorithm to identify the most important design factors in warm climate regions. By taking the average annual temperature, freezing index, maximum humidity and so on as input parameters, [PERSON] et al. [28] developed a prediction model for IRI using ANNs for flexible pavements located in wet-freeze, dry-freeze, wet no-freeze and dry no-freeze zones. Based on an adaptive network-based fuzzy inference system (ANFIS) and various meta-heuristic optimizations including the genetic algorithm (GA) and particle swarm optimization (PSO), [PERSON] et al. [29] proposed a new hybrid algorithm for IRI prediction and demonstrated the superiority of PSO-based algorithms to other algorithms for evaluating the quality of pavement roughness. Based on the analysis of the above research, the limitations of IRI prediction can be summarized as follows. Firstly, the study of pavement roughness is limited by the difficulty of acquiring and integrating data from multiple sources. Acquiring and integrating these data is challenging. Secondly, the factors influencing IRI are diverse and complicated. Most existing prediction models fail to identify the key factors. Including all variables from the dataset as inputs into the prediction model can lead to significant reductions in both computational efficiency and model accuracy. Third, ML models, especially ensemble learning and DL, are often referred to as 'black boxes'. Because their complex internal structures limit direct interpretability, it is difficult to directly explain the influence mechanisms of key factors on IRI. When the physical models are well defined and based on a solid mechanistic understanding, they are preferred. However, ML methods are particularly useful when the problem is nonlinear and complex and where physical models are either incomplete or computationally expensive. To address these issues, we compiled a pavement roughness dataset for the period 1989-2018 from the LTPP database. By achieving spatial and temporal alignment using the annual intervals as the temporal scale and the state code and unique identifiers as spatial references, this study developed a high-quality, multi-source dataset. Multicollinearity in the multi-source dataset was handled by the variance inflation factor (VIF). Subsequently, feature selection was performed to identify the key influencing factors of IRI using a voting strategy. On this basis, we proposed a novel IRI prediction model based on XGBoost and compared it with the RF and GBDT models. Finally, we utilized the interpretable methods to reveal the mechanism of the key factors. The main contributions of this study are as follows:  
1. In this study, a spatial-temporally aligned multi-source pavement roughness dataset is constructed. By integrating sample data on pavement structure, climate conditions, traffic load and pavement performance, the dataset provides a comprehensive basis for analysis. To identify the key factors influencing the IRI, a feature selection framework is developed that combines the mutual information method, the random forest-recursive feature elimination (RF-RFE) algorithm, the Lasso regression method and a voting-based strategy. 2. Based on the identified key factors, an IRI prediction model based on XGBoost is developed. Comparative analysis such as the mean absolute error (\(MAE\)), the mean absolute percentage error (\(MAPE\)), the root mean square error (\(RMSE\)), the coefficient of determination (\(R^{2}\)) and the comparison of actual and predicted IRI show that the XGBoost-based model achieves superior prediction performance. 3.