A hybrid method for winter road surface temperature prediction using improved LSTMs and stacking-based ensemble learning

Accurate prediction of road surface temperature (RST) is essential for proactive winter road maintenance and traffic safety management. However, existing approaches – ranging from physics-based models to data-driven methods – either require detailed pavement thermal parameters that are rarely available at operational road meteorological stations, or lack the capacity to simultaneously exploit local meteorological analogues and long-range temporal dependencies in an interpretable ensemble framework. This study proposes the Improved LSTMs Ensemble with Stacking (ILES) framework, which integrates two base learners with partially complementary predictive characteristics within a stacking ensemble employing out-of-fold cross-validation. The first base learner, KNN-LSTM, augments sequential modelling with similarity-based retrieval of historically analogous meteorological states to capture locally recurrent patterns. The second, BiLSTM-MHA, combines bidirectional recurrent processing with multi-head self-attention to extract long-range temporal dependencies across a 24 h input window. Moreover, a Bayesian Ridge Regression meta-learner fuses the base-learner outputs through Evidence Maximization, yielding probabilistic forecasts with closed-form posterior predictive uncertainty at the ensemble combination layer. The framework is trained and evaluated on four consecutive winter seasons (December 2020 to February 2024) of road meteorological observations from station M9393 in the northwest inland plain of Jiangsu Province, China. Results indicate that ILES achieves lower prediction errors in general than ten other models spanning persistence forecasting, traditional machine learning, and deep learning approaches, with R 2 reaching 0.993, 0.923, and 0.826 at 1, 3, and 6 h forecasting horizons, respectively. Among three input configurations evaluated, physics-motivated feature engineering incorporating the air–surface temperature gradient and multi-scale RST temporal tendencies outperforms both the station-only baseline and ERA5-Land reanalysis augmentation, indicating that domain-knowledge-guided feature construction provides a more effective and operationally practical input strategy at instrumented sites. SHAP-based interpretability analysis, stratified by temperature regime and diurnal cycle, confirms that the learned feature importance rankings are qualitatively consistent with the dominant drivers of RST evolution identified by surface energy balance theory. Multi-site generalization is further validated at two independent stations within the same temperate monsoon climate zone, confirming the transferability of the proposed framework across different road environments within this climate setting.