论文标题
空间 - 周期融合图神经网络，用于交通流量预测
Spatial-Temporal Fusion Graph Neural Networks for Traffic Flow Forecasting
论文作者
论文摘要
由于空间依赖性和不同道路之间时间模式的动态趋势，对交通流的空间数据预测是一项艰巨的任务。现有的框架通常利用给定的空间邻接图和复杂机制来建模空间和时间相关。但是，给定的空间图结构的有限表示，具有不完整的相邻连接可能会限制对这些模型的有效空间依赖性学习。为了克服这些局限性，我们的论文提出了空间融合图神经网络（STFGNN），以进行交通预测。 SFTGNN可以通过各种空间和时间图的新型融合操作有效地学习隐藏的时空依赖性，该操作是通过数据驱动方法生成的。同时，通过将此融合图模块和新型的封闭卷积模块整合到统一层中，SFTGNN可以处理长序列。几个公共流量数据集的实验结果表明，与其他基线相比，我们的方法始终如一地达到最先进的性能。
Spatial-temporal data forecasting of traffic flow is a challenging task because of complicated spatial dependencies and dynamical trends of temporal pattern between different roads. Existing frameworks typically utilize given spatial adjacency graph and sophisticated mechanisms for modeling spatial and temporal correlations. However, limited representations of given spatial graph structure with incomplete adjacent connections may restrict effective spatial-temporal dependencies learning of those models. To overcome those limitations, our paper proposes Spatial-Temporal Fusion Graph Neural Networks (STFGNN) for traffic flow forecasting. SFTGNN could effectively learn hidden spatial-temporal dependencies by a novel fusion operation of various spatial and temporal graphs, which is generated by a data-driven method. Meanwhile, by integrating this fusion graph module and a novel gated convolution module into a unified layer, SFTGNN could handle long sequences. Experimental results on several public traffic datasets demonstrate that our method achieves state-of-the-art performance consistently than other baselines.
