Pavement temperature is a critical factor in winter road maintenance as it directly affects operational decisions related to de-icing, antiicing, and other safety measures. Accurate forecasting of pavement temperature enables road agencies to optimize maintenance strategies, reduce operational costs, and improve roadway safety outcomes. This study proposes a novel machine-learning algorithm, termed LSTMCNN, which integrates convolutional neural networks (CNNs) with long short-term memory (LSTM) networks for pavement temperature prediction. The proposed model enables the LSTM component to capture sequential dependencies, whereas the CNN component extracts local and spatial features embedded in time-series temperature records. Therefore, the proposed model can effectively identify long-range temporal relationships while uncovering localized or spatial features within the dataset. The input data—comprising pavement, atmospheric, and soil temperatures—were obtained at the entrance of a tunnel where a multivehicle pile-up due to black ice had occurred previously. The proposed LSTM-CNN model achieved an average prediction error of 0.61 ℃ and was benchmarked against other well-established machine-learning models, including Transformer and standalone LSTM architectures. The results show that the proposed algorithm delivers statistically superior predictive performance. The LSTM-CNN approach offers significant potential for enhancing the efficiency and effectiveness of winter road maintenance operations.

ABSTRACT
1. Introductuon
2. Method
3. Data
4. Pavement Temperature Prediction
5. Evaluation
6. Conclusions and Future Studies
ACKNOWLEDGMENTS
REFERENCES

• Jang Jinhwan(한국건설기술연구원 도로교통연구본부 연구위원) | 장진환 Corresponding author