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.

中文翻译：

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TSSRGCN：用于交通流量预测的时域频谱空间检索图卷积网络

交通流量预测对于提高交通运输系统的效率和预防紧急情况具有重要意义。由于短期和长期交通流的高度非线性和复杂的演化模式，现有方法经常无法充分利用时空信息，尤其是具有不同时移和道路特征的各种时空模式段。此外，代表交通状态指示器绝对值的全局性和代表相对值的局部性没有被同时考虑。本文提出了一种神经网络模型，重点关注交通网络的全局性和局部性以及交通数据的时间模式。基于周期的膨胀可变形卷积块旨在精确捕获每个节点上的不同时变趋势。我们的模型可以提取全局和局部空间信息，因为我们结合了两种图卷积网络方法来学习节点和边的表示。在两个真实世界的数据集上进行的实验表明，该模型可以检查交通数据的时空相关性，并且其性能优于已比较的最新方法。进一步的分析表明，交通网络的局部性和全局性对于交通流的预测至关重要，所提出的TSSRGCN模型可以适应各种时间交通模式。我们的模型可以提取全局和局部空间信息，因为我们结合了两种图卷积网络方法来学习节点和边的表示。在两个真实世界的数据集上进行的实验表明，该模型可以检查交通数据的时空相关性，并且其性能优于已比较的最新方法。进一步的分析表明，交通网络的局部性和全局性对于交通流的预测至关重要，所提出的TSSRGCN模型可以适应各种时间交通模式。我们的模型可以提取全局和局部空间信息，因为我们结合了两种图卷积网络方法来学习节点和边的表示。在两个真实世界的数据集上进行的实验表明，该模型可以检查交通数据的时空相关性，并且其性能优于已比较的最新方法。进一步的分析表明，交通网络的局部性和全局性对于交通流的预测至关重要，所提出的TSSRGCN模型可以适应各种时间交通模式。