Long-term traffic prediction has always been a challenging task due to its
dynamic temporal dependencies and complex spatial dependencies. In this paper,
we propose a model that combines hybrid Transformer and spatio-temporal
self-supervised learning. The model enhances its robustness by applying
adaptive data augmentation techniques at the sequence-level and graph-level of
the traffic data. It utilizes Transformer to overcome the limitations of
recurrent neural networks in capturing long-term sequences, and employs
Chebyshev polynomial graph convolution to capture complex spatial dependencies.
Furthermore, considering the impact of spatio-temporal heterogeneity on traffic
speed, we design two self-supervised learning tasks to model the temporal and
spatial heterogeneity, thereby improving the accuracy and generalization
ability of the model. Experimental evaluations are conducted on two real-world
datasets, PeMS04 and PeMS08, and the results are visualized and analyzed,
demonstrating the superior performance of the proposed model.

利用混合 Transformer 和时空自监督学习的模型来提高长期交通预测的鲁棒性，该模型通过在交通数据的序列级和图级应用自适应数据增强技术来增强其鲁棒性，利用 Transformer 克服了循环神经网络在捕捉长期序列方面的局限性，并采用 Chebyshev 多项式图卷积来捕捉复杂的空间依赖关系。此外，考虑到时空异质性对交通速度的影响，我们设计了两个自监督学习任务来建模时空异质性，从而提高模型的准确性和泛化能力。在两个真实数据集 PeMS04 和 PeMS08 上进行了实验评估，结果进行了可视化和分析，证明了所提模型的卓越性能。