Accurate forecasting of traffic flow in the future period is very important for planning traffic routes and alleviating traffic congestion. However, traffic flow forecasting still faces serious challenges. Most of the existing traffic flow forecasting methods are static graph convolutional networks based on prior knowledge, ignoring the special spatial–temporal dynamics of spatial–temporal data. Using only adaptive dynamic graphs completely discards the objective and real spatial connectivity information in static graphs. To this end, we propose a novel information enhancement and dynamic-static fusion attention network (IEDSFAN). Firstly, the Multi-Graph Fusion Gating mechanism (MGFG) designed in IEDSFAN effectively fuses dynamic and static graphs to dynamically capture the hidden spatial–temporal correlation. Secondly, we construct a novel Gated Multi-head Self-Attention (GMHSA), which maps the input through the MGFG module to capture the complex spatial–temporal interactions in the features. Finally, we generate adaptive parameters to solve the problem that shared parameters cannot learn multiple traffic patterns, and enhance the expression of sequence information through the peak flag module. We conducted extensive experiments on five real-world traffic datasets, and the experimental results show that the performance of IEDSFAN is significantly better than all baselines.

准确预测未来一段时间的交通流量对于规划交通路线和缓解交通拥堵非常重要。然而，交通流量预测仍然面临严峻的挑战。现有的交通流预测方法大多是基于先验知识的静态图卷积网络，忽略了时空数据的特殊时空动态。仅使用自适应动态图会完全丢弃静态图中的客观和实际空间连通性信息。为此，我们提出了一种新的信息增强和动静态融合注意力网络 （IEDSFAN）。首先，IEDSFAN 中设计的多图融合门控机制 （MGFG） 有效地融合了动静态图，以动态捕捉隐藏的时空相关性;其次，我们构建了一个新的门控多头自我注意 （GMHSA），它通过 MGFG 模块映射输入以捕获特征中复杂的时空交互。最后，生成自适应参数解决共享参数无法学习多个流量模式的问题，并通过峰值标志模块增强序列信息的表达。我们对 5 个真实世界的交通数据集进行了广泛的实验，实验结果表明 IEDSFAN 的性能明显优于所有基线。