Nothing Special   »   [go: up one dir, main page]
Skip to main content

Advertisement

Springer Nature Link
Log in

A correlation information-based spatiotemporal network for traffic flow forecasting

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

Traffic flow forecasting technology plays an important role in intelligent transportation systems. Based on graph neural networks and attention mechanisms, most previous works utilize the transformer architecture to discover spatiotemporal dependencies and dynamic relationships. However, they have not thoroughly considered correlation information among spatiotemporal sequences. In this paper, based on the maximal information coefficient, we present two elaborate spatiotemporal representations, spatial correlation information (SCorr) and temporal correlation information (TCorr). Using SCorr, we propose a correlation information-based spatiotemporal network (CorrSTN) that includes a dynamic graph neural network component for integrating correlation information into spatial structure effectively and a multi-head attention component for modeling dynamic temporal dependencies accurately. Utilizing TCorr, we explore the correlation pattern among different periodic data to identify the most relevant data, and then design an efficient data selection scheme to further enhance model performance. The experimental results on the highway traffic flow (PEMS03, PEMS04, PEMS07 and PEMS08) and metro crowd flow (HZME inflow and outflow) datasets demonstrate that CorrSTN outperforms the state-of-the-art methods in terms of predictive performance. In particular, on the HZME (outflow) dataset, our model makes significant improvements compared with the ASTGNN model by 13.2%, 15.3% and 29.3% in the metrics of MAE, RMSE and MAPE, respectively.

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
$34.99 /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 excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

Data Availability

The datasets, code and pre-trained models generated and analyzed during the current study are available in the CorrSTN repository, https://github.com/bjtu-ccd-lab/CorrSTN.

References

  1. Muhammad AN, Aseere AM, Chiroma H, Shah H, Gital AY, Hashem IAT (2021) Deep learning application in smart cities: recent development, taxonomy, challenges and research prospects. Neural Comput Appl 33(7):2973–3009. https://doi.org/10.1007/s00521-020-05151-8

    Article  Google Scholar 

  2. Qureshi KN, Ahmad A, Piccialli F, Casolla G, Jeon G (2021) Nature-inspired algorithm-based secure data dissemination framework for smart city networks. Neural Comput Appl 33(17):10637–10656. https://doi.org/10.1007/s00521-020-04900-z

    Article  Google Scholar 

  3. Drucker H, Burges CJC, Kaufman L, Smola A, Vapnik V (1997) Support vector regression machines. In: Mozer MC, Jordan M, Petsche T (eds) Advances in neural information processing systems, vol 9. MIT Press, Cambridge, pp 155–161

    Google Scholar 

  4. Jeong Y-S, Byon Y-J, Castro-Neto MM, Easa SM (2013) Supervised weighting-online learning algorithm for short-term traffic flow prediction. IEEE Trans Intell Transp Syst 14(4):1700–1707. https://doi.org/10.1109/TITS.2013.2267735

    Article  Google Scholar 

  5. Sun Y, Leng B, Guan W (2015) A novel wavelet-svm short-time passenger flow prediction in Beijing subway system. Neurocomputing 166:109–121. https://doi.org/10.1016/j.neucom.2015.03.085

    Article  Google Scholar 

  6. Van Lint J, Van Hinsbergen C (2012) Short-term traffic and travel time prediction models. Artif Intell Appl Critic Transp Issues 22(1):22–41

    Google Scholar 

  7. Luo X, Li D, Yang Y, Zhang S (2019) Spatiotemporal traffic flow prediction with knn and lstm. J Adv Transp 2019:10. https://doi.org/10.1155/2019/4145353

    Article  Google Scholar 

  8. Cui Z, Ke R, Wang Y (2018) Deep bidirectional and unidirectional lstm recurrent neural network for network-wide traffic speed prediction. CoRR abs/180102143, 1–12

  9. Yao H, Wu F, Ke J, Tang X, Jia Y, Lu S, Gong P, Ye J, Li Z (2018) Deep multi-view spatial-temporal network for taxi demand prediction. In: proceedings of the AAAI conference on artificial intelligence, pp 2588–2595

  10. Zhang J, Zheng Y, Qi D, Li R, Yi X, Li T (2018) Predicting citywide crowd flows using deep spatio-temporal residual networks. Artif Intell 259:147–166

    Article  MathSciNet  MATH  Google Scholar 

  11. Wang J, Zhu W, Sun Y, Tian C (2021) An effective dynamic spatiotemporal framework with external features information for traffic prediction. Appl Intell 51(6):3159–3173. https://doi.org/10.1007/s10489-020-02043-1

    Article  Google Scholar 

  12. Yu B, Yin H, Zhu Z (2018) Spatio-temporal graph convolutional networks: a deep learning framework for traffic forecasting. In: proceedings of the international joint conference on artificial intelligence

  13. Li Y, Yu R, Shahabi C, Liu Y (2018) Diffusion convolutional recurrent neural network: data-driven traffic forecasting. In: international conference on learning representations

  14. Guo S, Lin Y, Feng N, Song C, Wan H (2019) 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 https://doi.org/10.1609/aaai.v33i01.3301922

  15. Song C, Lin Y, Guo S, Wan H (2020) 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 https://doi.org/10.1609/aaai.v34i01.5438

  16. Li M, Zhu Z (2021) Spatial-temporal fusion graph neural networks for traffic flow forecasting. In: proceedings of the AAAI conference on artificial intelligence vol 35, pp 4189–4196

  17. Guo S, Lin Y, Wan H, Li X (2021) Learning dynamics and heterogeneity of spatial-temporal graph data for traffic forecasting. IEEE Trans Knowledge Data Eng 99:1–1. https://doi.org/10.1109/tkde.2021.3056502

    Article  Google Scholar 

  18. Liu CH, Piao C, Ma X, Yuan Y, Tang J, Wang G, Leung K K (2021) Modeling citywide crowd flows using attentive convolutional lstm. In: IEEE international conference on data engineering (ICDE), pp 217–228 https://doi.org/10.1109/ICDE51399.2021.00026

  19. Wang Y, Yin H, Chen T, Liu C, Wang B, Wo T, Xu J (2021) Gallat: A spatiotemporal graph attention network for passenger demand prediction. In: IEEE international conference on data engineering (ICDE), pp 2129–2134 https://doi.org/10.1109/ICDE51399.2021.00212

  20. Zhou X, Shen Y, Huang L, Zang T, Zhu Y (2021) Multi-level attention networks for multi-step citywide passenger demands prediction. IEEE Trans Knowl Data Eng 33(5):2096–2108. https://doi.org/10.1109/TKDE.2019.2948005

    Article  Google Scholar 

  21. Wu Z, Pan S, Long G, Jiang J, Chang X, Zhang C (2020) Connecting the dots: multivariate time series forecasting with graph neural networks. In: proceedings of the 26th ACM SIGKDD international conference on knowledge discovery & data mining, pp 753–763 https://doi.org/10.1145/3394486.3403118

  22. Fang Z, Long Q, Song G, Xie K (2021) Spatial-temporal graph ode networks for traffic flow forecasting. In: proceedings of the 27th ACM SIGKDD conference on knowledge discovery & data mining, pp 364–373. Association for computing machinery, New York, USA https://doi.org/10.1145/3447548.3467430

  23. Zheng C, Fan X, Wang C, Qi J (2020) Gman: a graph multi-attention network for traffic prediction. In: proceedings of the AAAI conference on artificial intelligence vol 34, pp 1234–1241 (2020). https://doi.org/10.1609/aaai.v34i01.5477

  24. Han L, Du B, Sun L, Fu Y, Lv Y, Xiong H (2021) Dynamic and multi-faceted spatio-temporal deep learning for traffic speed forecasting. In: proceedings of the 27th ACM SIGKDD conference on knowledge discovery & data mining, pp 547–555. Association for computing machinery, New York, USA https://doi.org/10.1145/3447548.3467275

  25. Zivot, E., Wang, J.: Vector autoregressive models for multivariate time series. Modeling financial time series with S-PLUS®, 385–429 (2006)

  26. Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735

    Article  Google Scholar 

  27. Wu Z, Pan S, Long G, Jiang J, Zhang C (2019) Graph wavenet for deep spatial-temporal graph modeling. In: Kraus, S. (ed.) proceedings of the international joint conference on artificial intelligence, pp 1907–1913 https://doi.org/10.24963/ijcai.2019/264

  28. Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez A N, Kaiser L, Polosukhin I (2017) Attention is all you need. In: proceedings of the 31st international conference on neural information processing systems, pp 6000–6010. Curran Associates Inc

  29. He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: IEEE conference on computer vision and pattern recognition, pp 770–778 https://doi.org/10.1109/CVPR.2016.90

  30. Ba J L, Kiros J R, Hinton G E (2016) Layer normalization. arXiv preprint arXiv:1607.06450

  31. Bai L, Yao L, Li C, Wang X, Wang C (2020) Adaptive graph convolutional recurrent network for traffic forecasting. In: advances in neural information processing systems. advances in neural information processing systems, vol 33, pp 17804–17815

  32. Reshef DN, Reshef YA, Finucane HK, Grossman SR, McVean G, Turnbaugh PJ, Lander ES, Mitzenmacher M, Sabeti PC (2011) Detecting novel associations in large data sets. Science 334(6062):1518–1524. https://doi.org/10.1126/science.1205438

    Article  MATH  Google Scholar 

  33. Chen C, Petty K, Skabardonis A, Varaiya P, Jia Z (2001) Freeway performance measurement system: mining loop detector data. Transp Res Rec 1748(1):96–102. https://doi.org/10.3141/1748-12

    Article  Google Scholar 

  34. Cleveland WS (1979) Robust locally weighted regression and smoothing scatterplots. J Am Stat Assoc 74(368):829–836. https://doi.org/10.1080/01621459.1979.10481038

    Article  MathSciNet  MATH  Google Scholar 

Download references

Acknowledgements

This research is supported by the National Key R &D Program of China (No. 2021ZD0113002), National Natural Science Foundation of China (No. 62072292, 61572005, 61771058) and Fundamental Research Funds for the Central Universities of China (No. 2020YJS032). The support and resources from the Center for High Performance Computing at Beijing Jiaotong University are also gratefully acknowledged.

Author information

Authors and Affiliations

  1. Key Laboratory of Big Data & Artificial Intelligence in Transportation, Ministry of Education, Beijing, 100044, China

    Weiguo Zhu, Yongqi Sun, Xintong Yi, Yan Wang & Zhen Liu

  2. School of Computer and lnformation Technology, Beijing Jiaotong University, Beijing, 100044, China

    Weiguo Zhu, Yongqi Sun, Xintong Yi, Yan Wang & Zhen Liu

Authors
  1. Weiguo Zhu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yongqi Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xintong Yi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yan Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Zhen Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yongqi Sun.

Ethics declarations

Conflicts of interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work 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

Zhu, W., Sun, Y., Yi, X. et al. A correlation information-based spatiotemporal network for traffic flow forecasting. Neural Comput & Applic 35, 21181–21199 (2023). https://doi.org/10.1007/s00521-023-08831-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-023-08831-3

Keywords

Search

Navigation