Abstract
As an essential part of intelligent transportation, accurate traffic forecasting helps city managers make better arrangements and allows users to make reasonable travel plans. Current mainstream traffic forecasting models are developed based on spatial-temporal graph convolutional neural networks, in which appropriate graph structures must be generated in advance. However, most existing graph generation approaches learn graph structures based on local neighborhood relationships of urban nodes, which cannot capture complex dependencies over long spatial ranges. To solve the above problems, we propose Spatial-Temporal Graph Convolutional Neural Network (STLGCN) for long-term traffic forecasting, in which a novel graph generation method is developed by measuring multi-scale correlations among vertices. Meanwhile, a new graph convolution method is proposed for extracting valuable features and filtering out the irrelevant ones, which significantly optimizes the process of spatial information aggregation. Extensive experimental results on two real public traffic datasets, METR-LA and PEMS-BAY, demonstrate the superior performance of our algorithm.
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
References
Fattah, J., Ezzine, L., Aman, Z., Moussami, H., Lachhab, A.: Forecasting of demand using ARIMA model. Int. J. Eng. Bus. Manag. 10 (2018). https://doi.org/10.1177/1847979018808673
Xie, Y., Zhang, P., Chen, Y.: A fuzzy ARIMA correction model for transport volume forecast. Math. Problems Eng. 2021, 6655102, 10 p. (2021). https://doi.org/10.1155/2021/6655102
Kim, K.: Financial time series forecasting using support vector machines. Neurocomputing 551, 307–319 (2003). https://doi.org/10.1016/S0925-2312(03)00372-2
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA (2016). https://doi.org/10.1109/CVPR.2016.90
Szegedy, C., et al.: Going deeper with convolutions. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA, pp. 1–9 (2015). https://doi.org/10.1109/CVPR.2015.7298594
Veličković, P., Cucurull, G., Casanova, A., Romero, A., Liò, P., Bengio, Y.: Graph attention networks. In: International Conference on Learning Representations (2018). http://openreview.net/forum?id=rJXMpikCZ
Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. In: International Conference on Learning Representations (2016). http://openreview.net/forum?id=SJU4ayYgl
Bruna, J., Zaremba, W., Szlam, A., LeCun, Y.: Spectral networks and deep locally connected networks on graphs. In: 2nd International Conference on Learning Representations, ICLR, Banff, Canada (2014)
Michael, D., Xavier, B., Pierre, V.: Convolutional neural networks on graphs with fast localized spectral filtering. In: International Conference on Neural Information Processing Systems (NIPS), pp. 3844–3852 (2016)
Shi, X., Chen, Z., Wang, H., Yeung, D., Wong, W., Woo, W.: Convolutional LSTM network: a machine learning approach for precipitation nowcasting. In: International Conference on Neural Information Processing Systems (NIPS), 802–810 (2015)
Wu, Z., Pan, S., Long, G., Jiang, J., Zhang, C.: Graph WaveNet for deep spatial-temporal graph modeling. In: International Joint Conference on Artificial Intelligence, pp. 1907–1913 (2019). https://doi.org/10.5555/3367243.3367303
Atwood, J., Towsley, D.: Diffusion-convolutional neural networks. In: Neural Information Processing Systems, pp. 2001–2009, Barcelona, Spain (2016). https://doi.org/10.5555/3157096.3157320
Bai, L., Yao, L., Li, C., Wang, X., Wang, C.: Adaptive graph convolutional recurrent network for traffic forecasting. In: Neural Information Processing Systems (2020). https://doi.org/10.5555/3495724.3497218
Li, Y., Yu, R., Shahabi, C., Liu, Y.: Diffusion convolutional recurrent neural network: data-driven traffic forecasting. In: 6th International Conference on Learning Representations (ICLR) (2018). http://openreview.net/forum?id=SJiHXGWAZ
Yu, B., Yin, H., Zhu, Z.: Spatio-temporal graph convolutional networks: a deep learning framework for traffic forecasting. In: International Joint Conference on Artificial Intelligence (IJCAI) Stockholm, Sweden (2018). https://doi.org/10.5555/3304222.3304273
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 3401, 914–921 (2022). https://doi.org/10.1609/aaai.v34i01.5438
Zhao, L., et al.: T-GCN: a temporal graph convolutional network for traffic prediction. IEEE Trans. Intell. Transport. Syst. 21(9), 3848–3858 (2020). https://doi.org/10.1109/TITS.2019.2935152
Zhu, Y., et al.: A survey on graph structure learning: progress and opportunities. In: International Joint Conference on Artificial Intelligence (2021)
Franceschi, L., Niepert, M., Pontil, M., He, X.: Learning discrete structures for graph neural networks. In: International Conference on Machine Learning (ICML) pp. 1972–1978 (2019). https://proceedings.mlr.press/v97/franceschi19a.html
Gao, X., Hu, W., Guo, Z.: Exploring structure-adaptive graph learning for robust semi-supervised classification. In: 2020 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6, London, UK (2020). https://doi.org/10.1109/ICME46284.2020.9102726
Guo, S., Lin, Y., Li, S., Chen, Z., Wan, H.: Deep spatial-temporal 3D convolutional neural networks for traffic data forecasting. IEEE Trans. Intell. Transp. Syst. 2010, 3913–3926 (2019). https://doi.org/10.1109/TITS.2019.2906365
Shao, Z., et al.: Decoupled dynamic spatial-temporal graph neural network for traffic forecasting. Proc. VLDB Endow. 2733–2746 (2022). https://doi.org/10.14778/3551793.3551827
Jiang, J., Han, C., Zhao, W., Wang, J.: PDFormer: propagation delay-aware dynamic long-range transformer for traffic flow prediction. In: AAAI (2023)
Acknowledgement
This work is supported by the National Natural Science Foundation of China (NSFC) (Grant No. 52071312), and the Open Program of Zhejiang Lab (Grant No. 2019KE0AB03).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
Chen, X., Peng, P., Tang, H. (2024). STLGCN: Spatial-Temporal Graph Convolutional Network for Long Term Traffic Forecasting. In: Tan, Z., Wu, Y., Xu, M. (eds) Big Data Technologies and Applications. BDTA 2023. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 555. Springer, Cham. https://doi.org/10.1007/978-3-031-52265-9_4
Download citation
DOI: https://doi.org/10.1007/978-3-031-52265-9_4
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-52264-2
Online ISBN: 978-3-031-52265-9
eBook Packages: Computer ScienceComputer Science (R0)