Traffic prediction is crucial to the intelligent transportation system. However, accurate traffic prediction still faces challenges. It is difficult to extract dynamic spatial–temporal correlations of traffic flow and capture the specific traffic pattern for each sub-region. In this paper, a temporal attention recurrent graph convolutional neural network (TARGCN) is proposed to address these issues. The proposed TARGCN model fuses a node-embedded graph convolutional (Emb-GCN) layer, a gated recurrent unit (GRU) layer, and a temporal attention (TA) layer into a framework to exploit both dynamic spatial correlations between traffic nodes and temporal dependencies between time slices. In the Emb-GCN layer, node embedding matrix and node parameter learning techniques are employed to extract spatial correlations between traffic nodes at a fine-grained level and learn the specific traffic pattern for each node. Following this, a series of gated recurrent units are stacked as a GRU layer to capture spatial and temporal features from the traffic flow of adjacent nodes in the past few time slices simultaneously. Furthermore, an attention layer is applied in the temporal dimension to extend the receptive field of GRU. The combination of the Emb-GCN, GRU, and the TA layer facilitates the proposed framework exploiting not only the spatial–temporal dependencies but also the degree of interconnectedness between traffic nodes, which benefits the prediction a lot. Experiments on public traffic datasets PEMSD4 and PEMSD8 demonstrate the effectiveness of the proposed method. Compared with state-of-the-art baselines, it achieves 4.62% and 5.78% on PEMS03, 3.08% and 0.37% on PEMSD4, and 5.08% and 0.28% on PEMSD8 superiority on average. Especially for long-term prediction, prediction results for the 60-min interval show the proposed method presents a more notable advantage over compared benchmarks. The implementation on Pytorch is publicly available at https://github.com/csust-sonie/TARGCN.

交通预测对于智能交通系统至关重要。然而，准确的交通预测仍然面临挑战。提取交通流的动态时空相关性并捕获每个子区域的特定交通模式是很困难的。在本文中，提出了时间注意力循环图卷积神经网络（TARGCN）来解决这些问题。所提出的 TARGCN 模型将节点嵌入图卷积（Emb-GCN）层、门控循环单元（GRU）层和时间注意（TA）层融合到一个框架中，以利用流量节点之间的动态空间相关性和时间依赖性时间片之间。在Emb-GCN层中，采用节点嵌入矩阵和节点参数学习技术来提取细粒度的流量节点之间的空间相关性，并学习每个节点的特定流量模式。接下来，一系列门控循环单元堆叠为 GRU 层，以同时捕获过去几个时间片中相邻节点的流量的空间和时间特征。此外，在时间维度上应用注意力层来扩展 GRU 的感受野。 Emb-GCN、GRU 和 TA 层的结合促进了所提出的框架不仅利用时空依赖性，而且利用交通节点之间的互连程度，这对预测有很大帮助。在公共交通数据集PEMSD4和PEMSD8上的实验证明了该方法的有效性。与最先进的基线相比，PEMS03 平均优于 4.62% 和 5.78%，PEMSD4 平均优于 3.08% 和 0.37%，PEMSD8 平均优于 5.08% 和 0.28%。 特别是对于长期预测，60 分钟间隔的预测结果表明，所提出的方法比比较基准具有更显着的优势。 Pytorch 上的实现可在 https://github.com/csust-sonie/TARGCN 上公开获取。