提出动态图网络，提升多任务交通预测精度与鲁棒性
DG-STMTL: A Novel Graph Convolutional Network for Multi-Task Spatio-Temporal Traffic Forecasting
- 用静态+动态图融合机制，按任务自适应调整邻居关系
- 在两个真实数据集上优于现有方法，最高提升6.2%
- 适合交通预测、多任务学习研究者参考
时空交通预测在智能交通系统中至关重要。准确预测的关键在于建模复杂的时空依赖关系并适应数据中的固有动态特性。传统图卷积网络（GCNs）常受限于静态邻接矩阵带来的领域偏差，或可学习矩阵导致的特定模式过拟合。这一挑战在多任务学习（MTL）场景下更加复杂：尽管MTL可通过任务协同提升预测精度，但任务干扰问题也尤为显著。为此，本文提出一种新型多任务学习框架——动态分组时空多任务学习（DG-STMTL）。该框架设计了一种混合邻接矩阵生成模块，通过任务特异性门控机制融合静态与动态邻接矩阵；同时引入分组图卷积模块，增强时空依赖建模能力。我们在两个真实世界数据集上进行了大量实验，结果表明所提方法优于现有最先进模型，验证了其有效性和鲁棒性。
原文摘要 · Abstract (English)
Spatio-temporal traffic prediction is crucial in intelligent transportation systems. The key challenge of accurate prediction is how to model the complex spatio-temporal dependencies and adapt to the inherent dynamics in data. Traditional Graph Convolutional Networks (GCNs) often struggle with static adjacency matrices that introduce domain bias or learnable matrices that may be overfitting to specific patterns. This challenge becomes more complex when considering Multi-Task Learning (MTL). While MTL has the potential to enhance prediction accuracy through task synergies, it can also face significant hurdles due to task interference. To overcome these challenges, this study introduces a novel MTL framework, Dynamic Group-wise Spatio-Temporal Multi-Task Learning (DG-STMTL). DG-STMTL proposes a hybrid adjacency matrix generation module that combines static matrices with dynamic ones through a task-specific gating mechanism. We also introduce a group-wise GCN module to enhance the modelling capability of spatio-temporal dependencies. We conduct extensive experiments on two real-world datasets to evaluate our method. Results show that our method outperforms other state-of-the-arts, indicating its effectiveness and robustness.
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