# Cross-Regional Pavement Temperature Prediction Using Transfer Learning and Random Forest
## 1 Introduction  
Asphalt pavement, as a typical temperature-sensitive structure, is significantly influenced by variations in ambient temperature throughout its service life [1, 2, 3]. Temperature not only directly affects the mechanical behavior of pavement materials but also alters the internal stress state of the structure, potentially leading to various temperature-relateddistresses. For instance, rutting tends to occur under high temperatures, while cracking is more common under low-temperature conditions [4; 5; 6]. Moreover, daily temperature fluctuations and seasonal changes can cause uneven thermal responses within the pavement structure [7]. These effects are further intensified by the distinct environmental characteristics of different climatic zones, which greatly influence the long-term performance and durability of pavements [8]. Accurate temperature prediction can offer theoretical guidance for structural design, material selection, and mitigation of temperature-induced distresses. To address the above needs, researchers have developed a range of pavement temperature prediction methods [9]. These methods can be broadly categorized into two types. The first type is based on meteorological principles and numerical heat transfer models [10; 11; 12]. Such approaches generally offer good structural interpretability and regional adaptability, making the methods applicable across various pavement types and climatic conditions. However, the prediction accuracy depends on the precise calibration of the thermal parameters of pavement materials and the assumptions or simplifications adopted in the modeling process. Any mismatch in parameters or oversimplification may result in considerable prediction errors. The second category includes statistical and empirical models, such as regression-based techniques [13; 14; 15]. These approaches are simpler to implement and can effectively reflect the combined impact of multiple environmental factors. As a result, they are widely used in engineering applications. Nevertheless, most of these models are trained on data from specific regions, and their generalizability across regions remains limited. Some studies have attempted to improve adaptability by introducing region-specific correction factors [16; 17], but these require large volumes of local data, involve complex modeling steps, and lack scalability for broader applications. In this context, the rapid advancement of artificial intelligence (AI) offers promising alternatives. Machine learning and deep learning techniques have been increasingly applied to engineering prediction tasks, including pavement temperature field modeling [18; 19; 20]. Compared with traditional methods such as multiple nonlinear regression, machine learning models offer distinct performance advantages. These include the ability to capture complex nonlinear relationships, manage multicollinearity, and handle high-dimensional, multi-source data efficiently. Furthermore, ensemble techniques such as random forest provide strong generalization capability due to their robustness to outliers, resistance to overfitting, and ability to interpret variables' importance [21; 22; 23; 24; 25]. Unlike parametric regression models, random forest does not require strong assumptions about data distribution, offering greater modeling flexibility. However, differences in data distribution across regions still pose challenges for cross-regional applications. To address this issue, transfer learning has emerged as an effective cross-domain modeling strategy [26; 27]. By leveraging knowledge from a source domain, transfer learning improves feature learning in target domains with limited data. This approach has shown promising results in remote sensing, traffic forecasting, and other engineering fields [28; 29; 30], although its potential in pavement temperature prediction remains underexplored.