Multi-Modal Data Fusion and Deep Learning-Based Early-Warning System for Highway Slope Stability Monitoring Under Traffic Loading

Highway slope instability under coupled traffic and environmental loading poses critical threats to transportation safety in mountainous regions, where dynamic vehicular forces interact with complex geological conditions in ways that single-modality monitoring cannot fully resolve. This study proposes MMDF-DEWS, a multi-modal data fusion and deep learning-based early-warning system that, for the first time, treats quantified traffic-loading parameters as a first-class input modality alongside Interferometric Synthetic Aperture Radar (InSAR) displacement, Global Navigation Satellite System (GNSS) measurements, and embedded geotechnical sensor outputs. A hybrid Transformer–bidirectional LSTM backbone with hierarchical attention-guided fusion enables the model to capture both long-range temporal deformation trends and short-term dynamic responses triggered by heavy-vehicle passage. To guard against over-fitting on a limited number of instability events, we adopt chronological training/validation/test partitioning, five-fold cross-validation for hyper-parameter selection, stratified focal-loss training, and cross-dataset evaluation on two independent public benchmarks: the Three Gorges Reservoir Area Landslide Monitoring Dataset (TGRA-LMD) and the European Ground Motion Service Sentinel-1 (EGMS-S1) dataset. The framework outperforms six state-of-the-art baselines by 4.7–11.2% in F1-score, and ablation studies confirm that the explicit inclusion of traffic-loading features alone improves Warning-class recall by 6.3 percentage points, demonstrating a direct and physically grounded link between cyclic vehicular loading and slope-state prediction. The system satisfies operationally relevant engineering targets for warning lead time and false-alarm rate, and provides interpretable attention maps suitable for transportation-authority decision support.