[go: up one dir, main page]
More Web Proxy on the site http://driver.im/ Skip to main content
Springer Nature Link
Log in

Multi-level spatial-temporal fusion neural network for traffic flow prediction

  • Published:
Cluster Computing Aims and scope Submit manuscript

Abstract

In urban system management and public safety, accurate traffic flow forecasting is pivotal for real-world applications such as traffic control, resource-sharing scheduling platforms, and intelligent transportation systems. The challenge lies in effectively capturing complex spatio-temporal correlations. To address this, we introduce ST-DSTN, a novel spatial-temporal fusion attention-based deep neural network that significantly advances the current state-of-the-art. ST-DSTN utilizes three distinct temporal branches to capture temporal dependencies and a convolutional neural network to encapsulate spatial dependencies. Our innovative contribution includes a spatio-temporal fusion attention module, effectively modeling the dynamic spatio-temporal dependencies of traffic data, thus improving upon existing methods. Additionally, we propose a hierarchical fusion method that permeates the entire model, capturing complex correlations at various levels through multi-level fusion. Our comprehensive experiments on the BikeNYC dataset demonstrate ST-DSTN’s superior performance, supported by numerical results that substantiate its improvement over current methods, surpassing the DeepSTN+ model by 4.31%.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
£29.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (United Kingdom)

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Algorithm 1
Fig. 8
Fig. 9

Similar content being viewed by others

Data availability

The datasets used and analysed during the current study available from the corresponding author on reasonable request.

References

  1. Guo, S., Lin, Y., Feng, N.E.: 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)

  2. Chu, K.-F., Lam, A.Y.S., Li, V.: Deep multi-scale convolutional LSTM network for travel demand and origin-destination predictions. IEEE Trans. Intell. Transp. Syst. 21, 3219–3232 (2020)

    Article  Google Scholar 

  3. Tian, C., Chan, W.K.: Spatial-temporal attention wavenet: a deep learning framework for traffic prediction considering spatial-temporal dependencies. IET Intell. Transp. Syst. 15, 549–561 (2021)

    Article  Google Scholar 

  4. Xiaoming, S., Qi, H., Shen, Y., Wu, G., Yin, B.: A spatial-temporal attention approach for traffic prediction. IEEE Trans. Intell. Transp. Syst. (2020). https://doi.org/10.1109/TITS.2020.2983651

    Article  Google Scholar 

  5. Zhang, J., Zheng, Y., Sun, J., Qi, D.: Flow prediction in spatio-temporal networks based on multitask deep learning. IEEE Trans. Knowl. Data Eng. 32, 468–478 (2020)

    Article  Google Scholar 

  6. Du, B., Hu, X., Sun, L., Liu, J., Qiao, Y., Lv, W.: Traffic demand prediction based on dynamic transition convolutional neural network. IEEE Trans. Intell. Transp. Syst. 22, 1237–1247 (2021)

    Article  Google Scholar 

  7. Li, T., Zhang, J., Bao, K., Liang, Y., Li, Y., Zheng, Y.: AutoST: efficient neural architecture search for spatio-temporal prediction. In: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (2020)

  8. Cui, Z., Henrickson, K.C., Ke, R., Wang, Y.: Traffic graph convolutional recurrent neural network: a deep learning framework for network-scale traffic learning and forecasting. IEEE Trans. Intell. Transp. Syst. 21, 4883–4894 (2020)

    Article  Google Scholar 

  9. Chen, C., Liu, B., Wan, S., Qiao, P., Pei, Q.: An edge traffic flow detection scheme based on deep learning in an intelligent transportation system. IEEE Trans. Intell. Transp. Syst. 22, 1840–1852 (2021)

    Article  Google Scholar 

  10. Chen, C., Liu, Z., Wan, S., Luan, J., Pei, Q.: Traffic flow prediction based on deep learning in internet of vehicles. IEEE Trans. Intell. Transp. Syst. 22, 3776–3789 (2021)

    Article  Google Scholar 

  11. Liu, L., Zhen, J., Li, G., Zhan, G., Lin, L.: Dynamic spatial-temporal representation learning for traffic flow prediction. IEEE Trans. Intell. Transp. Syst. 22, 7169–7183 (2021)

    Article  Google Scholar 

  12. Li, M., Zhu, Z.: Spatial-temporal fusion graph neural networks for traffic flow forecasting. In: AAAI, pp. 4189–4196 (2021)

  13. 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. ASCE 129, 664–672 (2003)

    Article  Google Scholar 

  14. Zhao, L., Song, Y., Zhang, C., Liu, Y., Wang, P., Lin, T., Deng, M., Li, H.: T-GCN: a temporal graph convolutional network for traffic prediction. IEEE Trans. Intell. Transp. Syst. 21, 3848–3858 (2020)

    Article  Google Scholar 

  15. Yao, H., Tang, X., Wei, H., Zheng, G., Li, Z.: Revisiting spatial-temporal similarity: a deep learning framework for traffic prediction. In: AAAI, pp. 5668–5675 (2019)

  16. Zheng, C., Fan, X., Wang, C., Qi, J.: GMAN: a graph multi-attention network for traffic prediction. In: AAAI, pp. 1234–1241 (2020)

  17. Cao, D., Zeng, K., Wang, J., Sharma, P.K., Ma, X., Liu, Y., Zhou, S.: Bert-based deep spatial-temporal network for taxi demand prediction. IEEE Trans. Intell. Transp. Syst. 23, 9442–9454 (2022)

    Article  Google Scholar 

  18. Huang, F., Yi, P., Wang, J., Li, M., Peng, J., Xiong, X.: A dynamical spatial-temporal graph neural network for traffic demand prediction. Inf. Sci. 594, 286–304 (2022)

    Article  Google Scholar 

  19. Shin, Y.Y., Yoon, Y.: Incorporating dynamicity of transportation network with multi-weight traffic graph convolutional network for traffic forecasting. IEEE Trans. Intell. Transp. Syst. 23, 2082–2092 (2022)

    Article  Google Scholar 

  20. Zivot, E., Wang, J.: Vector autoregressive models for multivariate time series. In: Modeling Financial Time Series with S-Plus, pp. 369–413 (2003)

  21. Chen, R., Liang, C., Hong, W., Gu, D.-X.: Forecasting holiday daily tourist flow based on seasonal support vector regression with adaptive genetic algorithm. Appl. Soft Comput. 26, 435–443 (2015)

    Article  Google Scholar 

  22. Johansson, U., Boström, H., Löfström, T., Linusson, H.: Regression conformal prediction with random forests. Mach. Learn. 97, 155–176 (2014)

    Article  MathSciNet  Google Scholar 

  23. Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Yu, P.S.: A comprehensive survey on graph neural networks. IEEE Trans. Neural Netw. Learn. Syst. 32, 4–24 (2019)

    Article  MathSciNet  Google Scholar 

  24. Jin, G., Sha, H., Xi, Z., Huang, J.: Urban hotspot forecasting via automated spatio-temporal information fusion. Appl. Soft Comput. 136, 110087 (2023)

    Article  Google Scholar 

  25. Jin, G., Liang, Y., Fang, Y., Huang, J., Zhang, J., Zheng, Y.: Spatio-temporal graph neural networks for predictive learning in urban computing: a survey. arXiv. https://arxiv.org/abs/2303.14483 (2023)

  26. Jin, G., Xi, Z., Sha, H., Feng, Y., Huang, J.: Deep multi-view graph-based network for citywide ride-hailing demand prediction. Neurocomputing 510, 79–94 (2022)

    Article  Google Scholar 

  27. Jin, G., Cui, Y., Zeng, L., Tang, H., Feng, Y., Huang, J.: Urban ride-hailing demand prediction with multiple spatio-temporal information fusion network. Transp. Res. C Emerg. Technol. 117, 102665 (2020)

    Article  Google Scholar 

  28. Jin, G., Sha, H., Feng, Y., Cheng, Q., Huang, J.: GSEN: an ensemble deep learning benchmark model for urban hotspots spatiotemporal prediction. Neurocomputing 455, 353–367 (2021)

    Article  Google Scholar 

  29. Do, L.N.N., Vu, H.L., Vo, B.Q., Liu, Z., Phung, D.: An effective spatial-temporal attention based neural network for traffic flow prediction—sciencedirect. Transp. Res. C Emerg. Technol. 108, 12–28 (2019)

    Article  Google Scholar 

  30. Liu, Z., Zhang, R., Wang, C., Xiao, Z., Jiang, H.: Spatial-temporal conv-sequence learning with accident encoding for traffic flow prediction. IEEE Trans. Netw. Sci. Eng. 9, 1765–1775 (2021)

    Article  Google Scholar 

  31. Liu, Z., Wu, Z., Wang, M., Zhang, R.: Multi-view spatial-temporal model for travel time estimation. In: Proceedings of the 29th International Conference on Advances in Geographic Information Systems (2021)

  32. Zeng, H., Peng, Z., Huang, X., Yang, Y., Hu, R.: Deep spatio-temporal neural network based on interactive attention for traffic flow prediction. Appl. Intell. (2022). https://doi.org/10.1007/s10489-021-02879-1

    Article  Google Scholar 

  33. Zhang, J., Zheng, Y., Qi, D., Li, R., Yi, X.: DNN-based prediction model for spatio-temporal data. In: Proceedings of the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, pp. 1–4 (2016)

  34. Zhang, J., Zheng, Y., Qi, D.: Deep spatio-temporal residual networks for citywide crowd flows prediction. In: AAAI, pp. 1655–1661 (2017)

  35. 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. 20, 3913–3926 (2019)

    Article  Google Scholar 

  36. Zonoozi, A., Kim, J., Li, X., Cong, G.: Periodic-CRN: a convolutional recurrent model for crowd density prediction with recurring periodic patterns. In: IJCAI, pp. 3732–3738 (2018)

  37. Liang, Y., Ouyang, K., Sun, J., Wang, Y., Zhang, J., Zheng, Y., Rosenblum, D.S., Zimmermann, R.: Fine-grained urban flow prediction. Proc. Web Conf. 2021, 1833–1845 (2021)

    Google Scholar 

  38. Tian, Z.: Approach for short-term traffic flow prediction based on empirical mode decomposition and combination model fusion. IEEE Trans. Intell. Transp. Syst. 22, 5566–5576 (2021)

    Article  Google Scholar 

  39. Lv, M., Hong, Z., Chen, L., Chen, T., Zhu, T., Ji, S.: Temporal multi-graph convolutional network for traffic flow prediction. IEEE Trans. Intell. Transp. Syst. 22, 3337–3348 (2021)

    Article  Google Scholar 

  40. Yu, L., Du, B., Hu, X., Sun, L., Han, L., Lv, W.: Deep spatio-temporal graph convolutional network for traffic accident prediction. Neurocomputing 423, 135–147 (2021)

    Article  Google Scholar 

  41. Zheng, H., Lin, F., Feng, X., Chen, Y.: A hybrid deep learning model with attention-based Conv-LSTM networks for short-term traffic flow prediction. IEEE Trans. Intell. Transp. Syst. 22, 6910–6920 (2021)

    Article  Google Scholar 

  42. Qi, Y., Hossain, M.S., Nie, J., Li, X.: Privacy-preserving blockchain-based federated learning for traffic flow prediction. Future Gener. Comput. Syst. 117, 328–337 (2021)

    Article  Google Scholar 

  43. Lin, Z., Feng, J., Lu, Z., Li, Y., Jin, D.: DeepSTN+: context-aware spatial-temporal neural network for crowd flow prediction in metropolis. In: AAAI, vol. 33, pp. 1020–1027 (2019)

  44. Zhang, J., Zheng, Y., Qi, D., Li, R., Yi, X., Li, T.: Predicting citywide crowd flows using deep spatio-temporal residual networks. arXiv. https://arxiv.org/abs/1701.02543 (2017)

  45. Shi, X., Chen, Z., Wang, H., Yeung, D., Wong, W., Woo, W.: Convolutional LSTM network: a machine learning approach for precipitation nowcasting. In: Advances in Neural Information Processing Systems, vol. 28, pp. 802–810 (2015)

Download references

Funding

This paper is supported by the Science and Technology Project of the Jiangxi Provincial Department of Education (project number: GJJ201121).

Author information

Authors and Affiliations

  1. Jiangxi Science and Technology Normal University, Nanchang, 330038, China

    Zhiying Peng & Hao Zhao

  2. Jiangxi College of Foreign Studies, Nanchang, 330099, China

    Yixue Yang

Authors
  1. Zhiying Peng
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yixue Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hao Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

All authors reviewed the manuscript. ZP wrote the main manuscript text.

Corresponding author

Correspondence to Zhiying Peng.

Ethics declarations

Competing interests

The authors have no competing interests as defined by Nature Research, or other interests that might be perceived to influence the results and/or discussion reported in this paper.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Peng, Z., Yang, Y. & Zhao, H. Multi-level spatial-temporal fusion neural network for traffic flow prediction. Cluster Comput 27, 6689–6702 (2024). https://doi.org/10.1007/s10586-024-04296-8

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10586-024-04296-8

Keywords

Search

Navigation