Traffic prediction is a vital aspect of Intelligent Transportation Systems with widespread applications. The main challenge is accurately modeling the complex spatial and temporal relationships in traffic data. Spatial–temporal Graph Neural Networks (GNNs) have emerged as one of the most promising methods to solve this problem. However, several key issues have not been well addressed in existing studies. Firstly, traffic patterns have significant periodic trends, existing methods often overlook the importance of periodicity. Secondly, most methods model spatial dependencies in a static manner, which limits the ability to learn dynamic traffic patterns. Lastly, achieving satisfactory results for both long-term and short-term forecasting remains a challenge. To tackle the above problems, this paper proposes a Multi-level Multi-view Augmented Spatio-temporal Transformer (LVSTformer) for traffic prediction, which captures spatial dependencies from three different levels: local geographic, global semantic, and pivotal nodes, along with long- and short-term temporal dependencies. Specifically, we design three spatial augmented views to delve into the spatial information from above three levels. By combining three spatial augmented views with three parallel spatial self-attention mechanisms, the model can comprehensively captures spatial dependencies at different levels. We design a gated temporal self-attention mechanism to dynamically capture long- and short-term temporal dependencies. Furthermore, a spatio-temporal context broadcasting module is introduced between two spatio-temporal layers to ensure a well-distributed allocation of attention scores, alleviating overfitting and information loss, and enhancing the generalization ability and robustness of the model. A comprehensive set of experiments are conducted on six well-known traffic benchmarks, the experimental results demonstrate that LVSTformer achieves state-of-the-art performance compared to competing baselines, with the maximum improvement reaching up to 4.32%.

中文翻译：

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当多视图遇上多层次：一种用于交通预测的新型时空转换器


交通预测是智能交通系统的一个重要方面，应用广泛。主要挑战是准确建模交通数据中复杂的空间和时间关系。时空图神经网络 （GNN） 已成为解决此问题的最有前途的方法之一。然而，现有研究中尚未很好地解决几个关键问题。首先，流量模式具有显著的周期性趋势，现有方法往往忽视了周期性的重要性。其次，大多数方法以静态方式对空间依赖关系进行建模，这限制了学习动态交通模式的能力。最后，在长期和短期预测中取得令人满意的结果仍然是一个挑战。针对上述问题，本文提出了一种用于交通预测的多级多视图增强时空转换器 （LVSTformer），它从局部地理、全局语义和关键节点三个不同层次捕获空间依赖关系，以及长期和短期时间依赖关系。具体来说，我们设计了三个空间增强视图，从以上三个层次深入研究空间信息。通过将三个空间增强视图与三个并行的空间自注意力机制相结合，该模型可以全面捕捉不同层次的空间依赖关系。我们设计了一种门控时间自我注意机制来动态捕获长期和短期的时间依赖性。此外，在两个时空层之间引入时空上下文广播模块，确保注意力分数的均匀分配，减轻过拟合和信息损失，增强模型的泛化能力和鲁棒性。 在六个知名流量基准上进行了一套全面的实验，实验结果表明，与竞争对手的基线相比，LVSTformer 实现了最先进的性能，最大改进高达 4.32%。