Collaborative Optimisation of Grey‐Markov Modelling and Adversarial Meta‐Learning for Resilient Intelligent Transportation Systems

To enhance the robustness and interpretability of road‐accident prediction under sparse, noisy, and dynamically evolving conditions, this study proposes a Multimodal Grey‐Markov and Adversarial Meta‐Learning (MGMC‐AML) framework. The model integrates grey relational analysis for adaptive modality weighting, dynamic Markov state modelling for temporal‐spatial transition learning, and adversarial meta‐optimisation for rapid recovery from perturbations. A reinforcement‐enhanced dynamic partitioning mechanism is introduced to maintain structural consistency of road‐network topology during fluctuating traffic states. Extensive experiments on multi‐source traffic and weather datasets demonstrate that the proposed framework achieves superior predictive performance, with a Mean Absolute Error (MAE) of 2.34 incidents per day, a Segmentation Consistency Index (SCI) of 0.85, and a Modular Q value of 0.78, while maintaining an attack‐recovery time of 162 s. These results surpass state‐of‐the‐art baselines, including LSTM, GNN, and Transformer models, confirming both its precision and resilience under adversarial or uncertain sensing conditions. The proposed approach offers a unified and reproducible framework that bridges uncertainty modelling, adaptive fusion, and robust optimisation, providing a theoretical and practical foundation for resilient traffic‐risk forecasting in intelligent‐transportation systems.