Fine-Scaled Predictive Modeling of Road Surface Conditions and Temperature in Urban Areas

Road administrators require fine-scaled information regarding road surface conditions to ensure efficient operation during winter periods. However, conventional models offer low-resolution information at a scale comparable to meteorological meshes or the spatial configuration of road weather information systems. Additionally, few methods have been proposed for predicting road surface conditions specifically in urban areas, where roads frequently experience shading from surrounding buildings. This study proposes a statistical approach for predicting road surface temperature and conditions in urban road networks. The complicated accumulated distribution of solar radiation along each road is calculated and used as an effective explanatory variable that considers the complex shading effects of nearby structures. The proposed model adopts a Bayesian spatiotemporal hierarchical framework for predicting road surface temperature using a solar radiation variable. Furthermore, a spatial machine learning model is implemented to estimate road surface conditions. The model classifies icy road conditions into six distinct types, achieving a sensitivity of 0.7712 and a balanced accuracy of 0.8637. Ultimately, the model provides significant information required for decision-making processes aimed at ensuring efficient winter road management. These results indicate that the applicability of the proposed approach can extend beyond the studied area, demonstrating its potential for broader implementation.