Dynamic graph transformation with multi-task learning for enhanced spatio-temporal traffic prediction

Traffic prediction plays an essential role in intelligent transportation systems by supporting urban traffic management and public safety. A major challenge lies in addressing both the limitations of static assumptions and the inherent complexity they introduce when modeling dynamic and heterogeneous traffic systems. Traditional methods often simplify complex spatio-temporal data into a single-dimensional framework, potentially overlooking intricate node interactions and detailed network characteristics. This fundamental challenge manifests primarily in single-task approaches. When extended to multi-task learning scenarios, the complexity and limitations of this modeling challenge becomes more pronounced. To address these issues, this paper introduce a novel framework, Dynamic Graph Transformation with Multi-Task Learning (DGT-MTL) for spatio-temporal traffic prediction. DGT-MTL features a dynamic adjacency matrix generation module that balances static stability with dynamic flexibility. Additionally, it employs a multi-scale graph learning module to effectively capture fine-grained, latent features. An adaptive multi-task learning module is incorporated to uncover hidden correlations and dynamic relationships between road segments. Experiments conducted across six standard benchmarks demonstrate DGT-MTL's superior performance compared to contemporary approaches, achieving over 15 % improvements in both ROC-AUC and F1 score metrics. Further experiments demonstrate its effectiveness and robustness in handling complex traffic prediction.