Abstract
Spatial-temporal patterns have been applied in many areas, such as traffic forecasting, skeleton-based recognition, and so on. In such areas, researchers can convert the prior knowledge into graphs and combine the latent graph dependencies into original features to get better representation. However, few works focus on the underlying pattern in the original feature, and they cannot capture the flexible interaction both spatially and temporally. What is more, they often ignore the heterogeneity in spatial-temporal data. In this paper, we solve this problem by designing a novel model, Multi-view Cascading Spatial-temporal Graph Neural Network. Our model has a cascading structure to enhance interaction and capture heterogeneity. Also, it takes the differencing orders of flow data into account to get a better representation and contains specific coupled graphs designed based on the sliding window technique. Extensive experiments are conducted on four real-world datasets, demonstrating that our method achieves state-of-the-art performance and outperforms other baselines.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Bai, L., Yao, L., Li, C., Wang, X., Wang, C.: Adaptive graph convolutional recurrent network for traffic forecasting. arXiv preprint arXiv:2007.02842 (2020)
Berndt, D.J., Clifford, J.: Using dynamic time warping to find patterns in time series. In: KDD Workshop, Seattle, WA, USA, vol. 10, pp. 359–370 (1994)
Box, G.E., Pierce, D.A.: Distribution of residual autocorrelations in autoregressive-integrated moving average time series models. J. Am. Stat. Assoc. 65(332), 1509–1526 (1970)
Cover, T., Hart, P.: Nearest neighbor pattern classification. IEEE Trans. Inf. Theory 13(1), 21–27 (1967)
Fang, Z., Long, Q., Song, G., Xie, K.: Spatial-temporal graph ode networks for traffic flow forecasting. In: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp. 364–373 (2021)
Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025–1035 (2017)
He, Z., Chow, C.Y., Zhang, J.D.: STCNN: a spatio-temporal convolutional neural network for long-term traffic prediction. In: 2019 20th IEEE International Conference on Mobile Data Management (MDM), pp. 226–233. IEEE (2019)
Huber, P.J.: Robust estimation of a location parameter. In: Kotz, S., Johnson, N.L. (eds.) Breakthroughs in Statistics. Springer Series in Statistics, pp. 492–518. Springer, New York (1992). https://doi.org/10.1007/978-1-4612-4380-9_35
Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016)
Li, M., Zhu, Z.: Spatial-temporal fusion graph neural networks for traffic flow forecasting. arXiv preprint arXiv:2012.09641 (2020)
Liu, J., Shahroudy, A., Xu, D., Wang, G.: Spatio-temporal LSTM with trust gates for 3D human action recognition. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9907, pp. 816–833. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46487-9_50
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)
Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, pp. 5998–6008 (2017)
Veličković, P., Cucurull, G., Casanova, A., Romero, A., Lio, P., Bengio, Y.: Graph attention networks. arXiv preprint arXiv:1710.10903 (2017)
Wang, H., Xu, M., Zhu, F., Deng, Z., Li, Y., Zhou, B.: Shadow traffic: a unified model for abnormal traffic behavior simulation. Comput. Graph. 70, 235–241 (2018)
Williams, B.M., Hoel, L.A.: Modeling and forecasting vehicular traffic flow as a seasonal Arima process: theoretical basis and empirical results. J. Transp. Eng. 129(6), 664–672 (2003)
Wu, N., Wang, J., Zhao, W.X., Jin, Y.: Learning to effectively estimate the travel time for fastest route recommendation. In: Proceedings of the 28th ACM International Conference on Information and Knowledge Management, pp. 1923–1932 (2019)
Wu, N., Zhao, X.W., Wang, J., Pan, D.: Learning effective road network representation with hierarchical graph neural networks. In: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 6–14 (2020)
Wu, Z., Pan, S., Long, G., Jiang, J., Zhang, C.: Graph wavenet for deep spatial-temporal graph modeling. arXiv preprint arXiv:1906.00121 (2019)
Xingjian, S., Chen, Z., Wang, H., Yeung, D.Y., Wong, W.K., Woo, W.C.: Convolutional LSTM network: a machine learning approach for precipitation nowcasting. In: Advances in Neural Information Processing Systems, pp. 802–810 (2015)
Yu, B., Yin, H., Zhu, Z.: Spatio-temporal graph convolutional networks: a deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875 (2017)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Liu, Z., Fu, K., Liu, X. (2022). Multi-view Cascading Spatial-Temporal Graph Neural Network for Traffic Flow Forecasting. In: Pimenidis, E., Angelov, P., Jayne, C., Papaleonidas, A., Aydin, M. (eds) Artificial Neural Networks and Machine Learning – ICANN 2022. ICANN 2022. Lecture Notes in Computer Science, vol 13530. Springer, Cham. https://doi.org/10.1007/978-3-031-15931-2_50
Download citation
DOI: https://doi.org/10.1007/978-3-031-15931-2_50
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-15930-5
Online ISBN: 978-3-031-15931-2
eBook Packages: Computer ScienceComputer Science (R0)