Robust Road Accident Prediction via Multi-Multi modal fusionModal Grey Markov Chain and Adversarial Meta-Learning

Rapid urbanization and the proliferation of intelligent transportation systems have amplified the complexity of road accident prediction, where challenges of data sparsity, multi-modal heterogeneity, and dynamic environmental interference persist. To address these issues, this study proposes a robust prediction framework that integrates multi-modal grey Markov chains with adversarial meta-learning. The model adaptively allocates modality weights through grey relational analysis, enhancing sensitivity to extreme weather and semantic risk factors, while dynamic state partitioning ensures real-time adaptability of large-scale road networks.