Traffic flow forecasting is of great significance for improving the
efficiency of transportation systems and preventing emergencies. Due to the
highly non-linearity and intricate evolutionary patterns of short-term and
long-term traffic flow, existing methods often fail to take full advantage of
spatial-temporal information, especially the various temporal patterns with
different period shifting and the characteristics of road segments. Besides,
the globality representing the absolute value of traffic status indicators and
the locality representing the relative value have not been considered
simultaneously. This paper proposes a neural network model that focuses on the
globality and locality of traffic networks as well as the temporal patterns of
traffic data. The cycle-based dilated deformable convolution block is designed
to capture different time-varying trends on each node accurately. Our model can
extract both global and local spatial information since we combine two graph
convolutional network methods to learn the representations of nodes and edges.
Experiments on two real-world datasets show that the model can scrutinize the
spatial-temporal correlation of traffic data, and its performance is better
than the compared state-of-the-art methods. Further analysis indicates that the
locality and globality of the traffic networks are critical to traffic flow
prediction and the proposed TSSRGCN model can adapt to the various temporal
traffic patterns.

该论文提出了一种基于神经网络模型的交通流预测方法，该方法可以提取交通流数据的空间和时间信息，进而通过结合两种图卷积网络方法来学习节点和边的表示，并利用基于周期的膨胀可变卷积模块来准确捕获每个节点上不同的时变趋势，实验结果表明该模型可以更好地预测交通流，并适应不同的时态交通模式。