DiffG‐MTL: A Dynamic Multidiffusion Graph Network for Multitask Traffic Accident Prediction

Traffic accident prediction serves as a cornerstone of intelligent transportation systems, enabling proactive city‐wide control strategies and public safety interventions. Effective models must capture the evolving spatiotemporal propagation of risk while addressing heterogeneous data distributions across urban regions. Current approaches face significant limitations: fixed graph topologies fail to represent nonstationary accident patterns, while uniform task weighting leads to optimization bias toward data‐rich areas, ultimately constraining adaptability in adjacency construction and multihop spatial reasoning. To address these challenges, we propose a dynamic multidiffusion graph network with multitask learning (DiffG‐MTL) for city‐scale accident prediction. Specifically, a dynamic diffusion adjacency generation (DDAG) module constructs time‐varying, diffusion‐based adjacency matrices through multiple propagation pathways. A multiscale graph structure learning (MGSL) module captures multihop spatial relationships and temporal cues, while effectively highlighting anomalous traffic behaviors. To alleviate regional data imbalance, we introduce a dynamic multitask learning objective that adaptively redistributes learning focus using recall‐aware weighting and task‐level normalization. Comprehensive evaluations on six widely used datasets demonstrate that DiffG‐MTL consistently outperforms state‐of‐the‐art baselines across multiple evaluation metrics. Additional experiments validate its robustness and effectiveness in modeling complex spatiotemporal accident patterns.