Abstract
Spatial-temporal graph learning has been a critical approach to modeling complex dependencies between variables in multivariate time series such as traffic series. However, when modeling the spatial dependencies between traffic nodes, most existing approaches regard the predefined or adaptive graphs as static, overlooking the dynamic nature of realistic graphs that change over time. In addition, due to limitations in model complexity and information density, most models only consider short-term historical series for future series forecasting, failing to account for the periodicity of long-term series. Furthermore, spatial-temporal indistinguishability is also a challenge for many approaches. Aiming to address these problems, we propose a novel neural network framework Sampling Spatial-Temporal Attention Network (SSTAN) to effectively capture latent spatial and temporal dependencies. Firstly, a spatial encoder is proposed to learn multi-level dynamic graph structures. Secondly, a temporal encoding framework with long-term sampling and temporal encoders is designed to capture long-term periodic features that contain high-density information. Thirdly, with the global information of the entire graph and long-term series, our model overcomes the challenge of spatial-temporal indistinguishability to distinguish similar series with different latent patterns. Finally, experimental results on three real-world datasets not only demonstrate the superiority of our model over baselines on traffic forecasting but also illustrate our model’s effectiveness in spatial-temporal dependencies learning.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Abdi, H., Williams, L.J.: Principal component analysis. Wiley Interdiscip. Rev.: Comput. Stat. 2(4), 433–459 (2010)
Akaike, H.: Maximum likelihood identification of gaussian autoregressive moving average models. Biometrika 60(2), 255–265 (1973)
Bai, L., Yao, L., Li, C., Wang, X., Wang, C.: Adaptive graph convolutional recurrent network for traffic forecasting. Adv. Neural. Inf. Process. Syst. 33, 17804–17815 (2020)
Cao, D., et al.: Spectral temporal graph neural network for multivariate time-series forecasting. Adv. Neural. Inf. Process. Syst. 33, 17766–17778 (2020)
Chen, C., Petty, K., Skabardonis, A., Varaiya, P., Jia, Z.: Freeway performance measurement system: mining loop detector data. Transp. Res. Rec. 1748(1), 96–102 (2001)
Cho, K., Van Merriënboer, B., Bahdanau, D., Bengio, Y.: On the properties of neural machine translation: Encoder-decoder approaches. arXiv preprint arXiv:1409.1259 (2014)
Guo, S., Lin, Y., Feng, N., Song, C., Wan, H.: Attention based spatial-temporal graph convolutional networks for traffic flow forecasting. In: Proceedings of the AAAI Conference On Artificial Intelligence. vol. 33, pp. 922–929 (2019)
Han, L., Du, B., Sun, L., Fu, Y., Lv, Y., Xiong, H.: Dynamic and multi-faceted spatio-temporal deep learning for traffic speed forecasting. In: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp. 547–555 (2021)
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)
Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016)
Li, Y., Yu, R., Shahabi, C., Liu, Y.: Diffusion convolutional recurrent neural network: Data-driven traffic forecasting. arXiv preprint arXiv:1707.01926 (2017)
Lv, Y., Duan, Y., Kang, W., Li, Z., Wang, F.Y.: Traffic flow prediction with big data: a deep learning approach. IEEE Trans. Intell. Transp. Syst. 16(2), 865–873 (2014)
MacQueen, J.: Classification and analysis of multivariate observations. In: 5th Berkeley Symp. Math. Statist. Probability, pp. 281–297. University of California Los Angeles LA USA (1967)
Shao, Z., Zhang, Z., Wang, F., Wei, W., Xu, Y.: Spatial-temporal identity: A simple yet effective baseline for multivariate time series forecasting. In: Proceedings of the 31st ACM International Conference on Information and Knowledge Management, pp. 4454–4458 (2022)
Song, C., Lin, Y., Guo, S., Wan, H.: Spatial-temporal synchronous graph convolutional networks: A new framework for spatial-temporal network data forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence. vol. 34, pp. 914–921 (2020)
Stathopoulos, A., Karlaftis, M.G.: A multivariate state space approach for urban traffic flow modeling and prediction. Transp. Res. Part C: Emerg. Technol. 11(2), 121–135 (2003)
Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, vol. 30 (2017)
Wu, Z., Pan, S., Long, G., Jiang, J., Zhang, C.: Graph wavenet for deep spatial-temporal graph modeling. arXiv preprint arXiv:1906.00121 (2019)
Yu, B., Yin, H., Zhu, Z.: Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875 (2017)
Zhao, L., et al.: T-gcn: a temporal graph convolutional network for traffic prediction. IEEE Trans. Intell. Transp. Syst. 21(9), 3848–3858 (2019)
Zheng, C., Fan, X., Wang, C., Qi, J.: Gman: A graph multi-attention network for traffic prediction. In: Proceedings of the AAAI Conference on Artificial Intelligence. vol. 34, pp. 1234–1241 (2020)
Zivot, E., Wang, J.: Vector autoregressive models for multivariate time series. Modeling financial time series with S-PLUS®, pp. 385–429 (2006)
Acknowledgement
This work was supported by the National Natural Science Foundation of China (62272023, 51991391, 51991395).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Chen, M., Xu, Y., Han, L., Sun, L. (2023). Sampling Spatial-Temporal Attention Network for Traffic Forecasting. In: Jin, Z., Jiang, Y., Buchmann, R.A., Bi, Y., Ghiran, AM., Ma, W. (eds) Knowledge Science, Engineering and Management. KSEM 2023. Lecture Notes in Computer Science(), vol 14118. Springer, Cham. https://doi.org/10.1007/978-3-031-40286-9_11
Download citation
DOI: https://doi.org/10.1007/978-3-031-40286-9_11
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-40285-2
Online ISBN: 978-3-031-40286-9
eBook Packages: Computer ScienceComputer Science (R0)