Snowfall and road surface freezing cause traffic disruptions and skidding accidents. When widespread extreme cold events or sudden heavy snowfalls occur, the continuous monitoring and management of extensive road networks until the restoration of traffic operations is constrained by the limited personnel and resources available to road authorities. Consequently, road surface condition prediction models have become increasingly necessary to enable timely and sustainable decision-making. This study proposes a road surface condition prediction model based on CCTV images collected from roadside cameras. Three databases were constructed based on different definitions of moisture-related surface classes, and models with the same architecture were trained and evaluated. The results showed that the best performance was achieved when ice and snow were combined into a single class rather than treated separately. The proposed model was designed with a simplified structure to ensure applicability in practical operations requiring computational efficiency. Compared with transfer learning using deeper and more complex pre-trained models, the proposed model achieved comparable prediction accuracy while requiring less training time and computational resources. These findings demonstrate the reliability and practical utility of the developed model, indicating that its application can support sustainable snow removal decision-making across extensive road networks.

Snowfall and road surface freezing cause traffic disruptions and skidding accidents. When widespread extreme cold events or sudden heavy snowfalls occur, the continuous monitoring and management of extensive road networks until the restoration of traffic operations is constrained by the limited personnel and resources available to road authorities. Consequently, road surface condition prediction models have become increasingly necessary to enable timely and sustainable decision-making. This study proposes a road surface condition prediction model based on CCTV images collected from roadside cameras. Three databases were constructed based on different definitions of moisture-related surface classes, and models with the same architecture were trained and evaluated. The results showed that the best performance was achieved when ice and snow were combined into a single class rather than treated separately. The proposed model was designed with a simplified structure to ensure applicability in practical operations requiring computational efficiency. Compared with transfer learning using deeper and more complex pre-trained models, the proposed model achieved comparable prediction accuracy while requiring less training time and computational resources. These findings demonstrate the reliability and practical utility of the developed model, indicating that its application can support sustainable snow removal decision-making across extensive road networks.