A Hybrid Method for Winter Road Surface Temperature Prediction Using Improved LSTMs and Stacking-Based Ensemble Learning

Abstract. Wintertime low temperatures and snow cover usually diminish the friction coefficient of asphalt pavements, thereby elevating accident and congestion risks. Road surface temperature (RST) is an important parameter for maintaining traffic safety under extreme winter weather conditions, as it helps predict road icing events. Aiming to enhance the precision and robustness of RST prediction, this paper introduces a forecasting framework combining optimized Long Short-Term Memory (LSTM) architectures with a stacking-based ensemble strategy. Two improved LSTMs are constructed: (1) KNN-LSTM, integrating K-nearest neighbors to capture local spatiotemporal similarity patterns, and (2) Attention-BiLSTM, employing bidirectional temporal modeling with dynamic attention weighting mechanisms. These models function as base learners in the stacking ensemble, with Bayesian ridge regression utilized as the meta-learner to consolidate their predictions. The proposed hybrid model was trained and validated using minute-resolution winter meteorological data (2020–2024) collected from a station located on the Longhai Railway Bridge in Jiangsu, China. Experimental results show that the KNN-LSTM and Attention-BiLSTM models exhibit complementary advantages in capturing localized and global temporal features. The ensemble model demonstrates superior performance over individual models, achieving a 1-hour MAE of 0.074, MSE of 0.010, and MAPE of 46.7 % with a significant reduction compared with the best-performing single model. Under extended prediction horizons (3-hour and 6-hour), including low-temperature below 0 °C conditions and typical weather backgrounds, the ensemble model sustains high prediction accuracy and stability. These findings underscore the efficacy of integrating local pattern extraction with attention-based mechanisms via ensemble learning, thereby enhancing RST prediction. This study presents a scalable and adaptable framework for intelligent road weather management systems, offering practical insights for operational deployment.