Hybrid Transformer and Spatial-Temporal Self-Supervised Learning for
  Long-term Traffic Prediction
Wang Zhu Doudou Zhang Baichao Long Jianli Xiao
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.