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.
翻译:由于不同道路之间的空间依赖性和时间模式动态趋势复杂,对交通流量进行空间时空数据预报是一项艰巨的任务,因为不同道路之间的空间依赖性和时间模式动态趋势复杂,现有框架通常使用特定空间相邻图和复杂的空间和时间关联模型机制,但相邻连接不完全的空间图结构的有限表现可能会限制这些模型的有效空间时空依赖性学习。为了克服这些限制,我们的论文提议空间时际组合图神经网络(STFGNN)用于交通流量预测。 SFTGNN可以通过数据驱动方法生成的各种空间和时间图的新型融合操作,有效地学习隐藏的空间时空依赖性。与此同时,通过将这种聚变图模块和新的进门变模块整合到一个统一的层次,SFTGNN可以处理长序列。 几个公共流量数据集的实验结果表明,我们的方法取得了比其他基线一致的状态性表现。