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STGHTN: Spatial-temporal gated hybrid transformer network for traffic flow forecasting

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Abstract

Accurate traffic forecasting is a critical function of intelligent transportation systems, which remains challenging due to the complex spatial and temporal dependence of traffic data. GNN-based traffic forecasting models typically utilize predefined graphical structures based on prior knowledge and do not adapt well to dynamically changing traffic characteristics, which may limit their performance. The transformer is a compelling architecture with an innate global self-attention mechanism, but cannot capture low-level detail very well. In this paper, we propose a novel Spatial-Temporal Gated Hybrid Transformer Network (STGHTN), which leverages local features from temporal gated convolution, spatial gated graph convolution respectively and global features by transformer to further improve the traffic flow forecasting results. First, in the temporal dimension, we take full advantage of the local properties of temporal gated convolution and the global properties of transformer to effectively fuse short-term and long-term temporal dependence. Second, we mutually integrate two modules to complement each representation by utilizing spatial gated graph convolution to extract local spatial dependence and transformer to extract global spatial dependence. Furthermore, we propose a multi-graph model that constructs a road connection graph, a similarity graph, and an adaptive dynamic graph to exploit the static and dynamic associations between road networks. Experiments on four real datasets confirm the proposed method’s state-of-the-art performance. Our implementation of the STGHTN code via PyTorch is available at https://github.com/JianSoL/STGHTN.

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China (Grant No. 61762092); the Open Foundation of the Key Laboratory in Software Engineering of Yunnan Province (Grant No. 2020SE303); the Major Science and Technology Project of Precious Metal Materials Genome Engineering in Yunnan Province (Grant No. 2019ZE001-1 and 202002AB080001-6); Yunnan provincial major science and technology: Research and Application of key Technologies for Resource Sharing and Collaboration Toward Smart Tourism (Grant No. 202002AD080047); and the Postgraduate Scientific Research Innovation Project of Hunan Province.

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  1. School of Software, Yunnan University, Chenggong District, Kunming, 650500, Yunnan, China

    Jiansong Liu, Yan Kang, Hao Li, Haining Wang & Xuekun Yang

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  1. Jiansong Liu
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  2. Yan Kang
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  3. Hao Li
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Correspondence to Yan Kang.

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Liu, J., Kang, Y., Li, H. et al. STGHTN: Spatial-temporal gated hybrid transformer network for traffic flow forecasting. Appl Intell 53, 12472–12488 (2023). https://doi.org/10.1007/s10489-022-04122-x

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