Hybrid Transformer and Spatial-Temporal Self-Supervised Learning for Long-term Traffic Prediction
This work addresses traffic prediction for urban planning and management, but it is incremental as it builds on existing Transformer and self-supervised learning methods.
The paper tackled long-term traffic prediction by proposing a hybrid Transformer and spatio-temporal self-supervised learning model, which achieved superior performance on PeMS04 and PeMS08 datasets.
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.