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

Abstract

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.