## 5 Conclusions  
Road maintenance and management are complex due to limited resources and extensive pavement networks. Predicting pavement performance using data-driven methods offers a comprehensive approach to improving maintenance strategies. The objective of this study is to achieve an accurate prediction of IRI using advanced ML techniques, providing scientific support for maintenance. This study introduced an explainable boosting method based on XGBoost, which effectively identifies key influencing factors and predicts IRI. We constructed a high-quality, multi-dimensional pavement roughness dataset using the LTPP database. After the voting-based feature selection, the key factors influencing IRI were identified. Through the comparison of ML models, the proposed XGBoost-based IRI prediction model achieved an \(R^{2}\) value of 0.778. Using explainable methods such as feature importance, SHAP and PDPs, this study revealed the specific mechanism of each influencing factor on IRI. The following conclusions can be drawn:  
1. **Key factor identification**. The integrated feature selection method results identified six key factors that affect the pavement roughness the most: ESAL, FREEZE_THAW, FREEZE_INDEX, TEMP, PRECIPITATION and MAINTENANCE. 2. **Significance of key factors analysis**. For IRI analysis, ESAL has a significant influence on the pavement roughness. And climate factors should be considered as part of pavement roughness. Although PRECIPITATION and MAINTENANCE exhibit relatively low direct contributions in the model, their roles in maintaining pavement roughness and extending the service life of the road should not be ignored. 3. **Interpretable analysis**. Through the SHAP and PDP analysis based on XGBoost, we can conclude that high traffic loads, frequent freeze-thaw cycles and large freezing indices significantly contribute to pavement unevenness. Under extreme temperature conditions, both high and low temperatures can exacerbate pavement roughness, emphasizing the importance of accounting for temperature change in pavement management strategies. Practically, the identification of key influencing factors and the explainable analysis provide data-driven support for the pavement management department to develop maintenance strategies. The developed XGBoost-based IRI prediction model can guide professionals to make more informed decisions about road maintenance. For example, in areas with extreme climatic conditions, such as low- and high-temperature areas, pavement materials with excellent resistance to cracking and thermal aging, respectively, should be used to reduce the adverse effects of extreme temperatures on pavements. In areas with frequent freeze-thaw cycles and high freeze index, it is necessary to strengthen the design of the waterproof layer of the road surface and ensure the normal operation of the drainage system. Although this study has considered pavement structure, climate and traffic factors, it does not account for temporal continuity in the IRI prediction model. Future research should use historical IRI values to construct a more time-sensitive prediction model. At the same time, customizing a fine-grained dataset with labels reflecting geographic or climatic conditions and thus enabling the model to provide tailored recommendations for construction and maintenance strategies is worth further in-depth study. In order to present a broader perspective on feature impact and interaction, integrating newer explainability techniques (e.g., LIME [56]) alongside SHAP would be part of the future extension of the research. Data curation: B.L. and B.D. ; Project administration: B.L. and [PERSON]. ([PERSON]); Methodology: B.L. and H.G. ([PERSON]); Formal analysis: [PERSON]. ([PERSON]) and B.D. ; Software: [PERSON]. ([PERSON]) and [PERSON] ([PERSON]); Writing--original draft: [PERSON]. ([PERSON]) and [PERSON]; Writing--review and editing: [PERSON]. ([PERSON]), [PERSON] ([PERSON]) and [PERSON] All authors have read and agreed to the published version of the manuscript.