# Cross-Regional Pavement Temperature Prediction Using Transfer Learning and Random Forest
## 5 Conclusions  
To address the poor adaptability of traditional models in different climatic regions, a transfer learning model based on random forest was proposed for improving the accuracy of pavement temperature prediction under cross-regional conditions. Firstly, a correlation analysis was conducted between meteorological factors and pavement temperature at various depths. Secondly, the meteorological factors were selected as the model inputs, and the random forest model was built with the pavement temperatures at different depths as the prediction targets. Thirdly, a transfer learning approach was applied. The model was optimized by using the feature enhancement strategy to improve its generalization ability in the target regions. This enabled high-precision temperature prediction across different regions. The main conclusions of this study are as follows:  
1. The correlation analysis reveals both commonalities and regional differences in the relationships between pavement temperature and meteorological factors. Across all study areas, pavement temperature consistently exhibits strong correlations with air temperature and solar radiation, indicating that these two factors are the dominant drivers of pavements' thermal behavior. However, the influence of relative humidity and wind speed varies among regions. At Site01 and Site03, the pavement temperature is significantly affected by both relative humidity and wind speed, whereas at Site02 and Site04, the correlation with relative humidity is weak. These regional variations highlight the need to consider localized climatic characteristics when developing and applying predictive models. 2. The random forest model achieves high accuracy when predicting pavement temperatures within the same region. For example, using meteorological data from Site01 to predict temperatures at Site01 yields an R\({}^{2}\) of 0.98, indicating excellent performance. However, when the same model is applied to Site02, Site03, and Site04, its accuracy declines significantly, with the \(R^{2}\) values falling to 0.72, 0.82, and 0.51, respectively. This performance gap highlights the model's strong reliance on region-specific data and its limited ability to generalize across varying climatic conditions. As a result, it is essential to improve the model's adaptability to ensure accurate and scalable pavement temperature predictions across multiple regions. 3. By introducing feature enhancement strategies based on multi-regional data, the TL-RF model demonstrates significant improvements in predictive performance. Compared to the RF model, the TL-RF model reduces the RMSE from 6.7 to 3.86 and the MAE from 4.91 to 2.78, while the \(R^{2}\) increases markedly from 0.75 to 0.92. These improvements reflect the model's enhanced generalization ability and robustness in adapting to regional variability. The findings demonstrate that the proposed approach provides a practical and scalable solution to the limitations of traditional models in cross-regional pavement temperature prediction. 4. To evaluate the generalization capability and practical applicability of the proposed TL-RF model, independent validation was performed using data from a region excluded from the training process. The model maintained high prediction accuracy across various depths, with an average R\({}^{2}\) of 0.95, indicating strong transferability and robustness under unfamiliar climatic conditions. While these results are promising, future research should explore the model's scalability to broader climatic regions and finer temporal resolutions. Utilizing public datasets and integrating deep learning-based ensemble methods may further improve the computational efficiency and predictive performance. **Author Contributions:** Conceptualization, J.Y., L.S. and Y.C. ; Data Curation, J.Y. and M.L. ; Formal Analysis, J.Y. ; Funding Acquisition, H.C.; Investigation, R.Y. and M.L. ; Methodology, T.J.; Resources, H.C.; Software, R.Y. ; Supervision, L.S. and Y.C.