Hybrid Transformer and Spatial-Temporal Self-Supervised Learning for Long-term Traffic Prediction

• URL: http://arxiv.org/abs/2401.16453v1
• Date: Mon, 29 Jan 2024 06:17:23 GMT
• Title: Hybrid Transformer and Spatial-Temporal Self-Supervised Learning for Long-term Traffic Prediction
• Authors: Wang Zhu, Doudou Zhang, Baichao Long, Jianli Xiao
• Abstract summary: We propose a model that combines hybrid Transformer and self-supervised learning. The model enhances its adaptive data augmentation by applying data augmentation techniques at the sequence-level of the traffic. We design two self-supervised learning tasks to model the temporal and spatial dependencies, thereby improving the accuracy and ability of the model.
• Score: 1.8531577178922987
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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