Skip to main content

Advertisement

Springer Nature Link
Log in

Spatial-temporal clustering enhanced multi-graph convolutional network for traffic flow prediction

  • Published:
Applied Intelligence Aims and scope Submit manuscript

Abstract

Dynamics and uncertainty are the fundamental reasons for the difficulty in accurately predicting traffic flow. In recent years, graph convolutional networks have been widely used in traffic flow prediction because of their excellent dynamic feature mapping ability. However, the existing models usually overlook the correlations among the nodes and the complex impact of external factors on traffic flow, which make it challenging to explore the complex spatial-temporal features. To overcome these shortcomings, we propose a novel Spatial-temporal Clustering enhanced Multi-Graph Convolutional Network (SCM-GCN) for traffic flow prediction. First, a Spatial-Temporal Clustering (STS) module based on the improved adjacency matrix DBSCAN clustering algorithm is constructed, this module divides traffic nodes into multiple highly correlated clusters, each of which consists of multi-graph features and time-varying features. Then, a Multi-Graph Spatial Feature Extraction (MGSFE) module that integrates the graph convolution operation and attention mechanism is designed to extract dynamic spatial features of multi-graph and time-varying features. Next, the Time-Varying Feature Extraction (TVFE) module based on the dilated convolution and gated attention mechanism is constructed. It integrates the output of the MGSFE module to extract dynamic temporal features of time-varying features and output the predicted values. Finally, the comparison and ablation experiments on four datasets show that the proposed model performs better than state-of-the-art models. The key source code and data are available at https://github.com/Bounger2/SCMGCN.

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

Similar content being viewed by others

Explore related subjects

Discover the latest articles and news from researchers in related subjects, suggested using machine learning.

Data availability

The key source code and data are available at https://github.com/Bounger2/SCMGCN.

References

  1. Li L, Jiang R, He Z et al (2020) Trajectory data-based traffic flow studies: a revisit. Transp Res Part C: Emerg Technol 114:225–240. https://doi.org/10.1016/j.trc.2020.02.016

    Article  Google Scholar 

  2. Yang S, Du M, Chen Q (2021) Impact of connected and autonomous vehicles on traffic efficiency and safety of an on-ramp. Simul Model Pract Theory 113:1–15. https://doi.org/10.1016/j.simpat.2021.102374

    Article  Google Scholar 

  3. Retallack AE, Ostendorf B (2019) Current understanding of the effects of congestion on traffic accidents. Int J Environ Res Public Health 16:1–13. https://doi.org/10.3390/ijerph16183400

    Article  Google Scholar 

  4. Elamrani Abou Elassad Z, Mousannif H, Al Moatassime H (2020) A proactive decision support system for predicting traffic crash events: a critical analysis of imbalanced class distribution. Knowl Based Syst 205:1–14. https://doi.org/10.1016/j.knosys.2020.106314

    Article  Google Scholar 

  5. Guidoni DL, Maia G, Souza FSH et al (2020) Vehicular traffic management based on traffic engineering for vehicular ad hoc networks. IEEE Access 8:45167–45183. https://doi.org/10.1109/ACCESS.2020.2978700

    Article  Google Scholar 

  6. Hou Q, Leng J, Ma G et al (2019) An adaptive hybrid model for short-term urban traffic flow prediction. Physica A 527:1–10. https://doi.org/10.1016/j.physa.2019.121065

    Article  Google Scholar 

  7. Pan YA, Guo J, Chen Y et al (2022) Incorporating traffic flow model into a deep learning method for traffic state estimation: a hybrid stepwise modeling framework. J Adv Transp 2022:1–17. https://doi.org/10.1155/2022/5926663

    Article  Google Scholar 

  8. Li M, Micheli A, Wang YG et al (2024) Guest editorial: deep neural networks for graphs: theory, models, algorithms, and applications. IEEE Trans Neural Networks Learn Syst 35:4367–4372. https://doi.org/10.1109/TNNLS.2024.3371592

    Article  Google Scholar 

  9. Ma X, Dai Z, He Z et al (2017) Learning traffic as images: a deep convolutional neural network for large-scale transportation network speed prediction. Sens (Switzerland) 17:1–16. https://doi.org/10.3390/s17040818

    Article  Google Scholar 

  10. Belhadi A, Djenouri Y, Djenouri D, Lin JCW (2020) A recurrent neural network for urban long-term traffic flow forecasting. Appl Intell 50:3252–3265. https://doi.org/10.1007/s10489-020-01716-1

    Article  Google Scholar 

  11. Zhang Y, Lu Z, Wang J, Chen L (2023) FCM-GCN-based upstream and downstream dependence model for air traffic flow networks. Knowl Based Syst 260:1–11. https://doi.org/10.1016/j.knosys.2022.110135

    Article  Google Scholar 

  12. Han X, Gong S (2022) LST-GCN: long short-term memory embedded graph convolution network for traffic flow forecasting. Electronics 11:1–14. https://doi.org/10.3390/electronics11142230

    Article  Google Scholar 

  13. Li J, Zheng R, Feng H et al (2024) Permutation equivariant graph framelets for heterophilous graph learning. IEEE Trans Neural Networks Learn Syst 35:11634–11648. https://doi.org/10.1109/TNNLS.2024.3370918

    Article  MathSciNet  Google Scholar 

  14. Ali A, Zhu Y, Zakarya M (2022) Exploiting dynamic spatio-temporal graph convolutional neural networks for citywide traffic flows prediction. Neural Netw 145:233–247. https://doi.org/10.1016/j.neunet.2021.10.021

    Article  Google Scholar 

  15. Huang X, Ye Y, Wang C et al (2022) A multi-mode traffic flow prediction method with clustering based attention convolution LSTM. Appl Intell 52:14773–14786. https://doi.org/10.1007/s10489-021-02770-z

    Article  Google Scholar 

  16. Guo C, Chen C-H, Hwang F-J et al (2022) Fast spatiotemporal learning framework for traffic flow forecasting. IEEE Trans Intell Transp Syst 1–11:1. https://doi.org/10.1109/TITS.2022.3224039

    Article  Google Scholar 

  17. Schubert E, Sander J, Ester M et al (2017) DBSCAN revisited, revisited: why and how you should (still) use DBSCAN. ACM Trans Database Syst 42:1. https://doi.org/10.1145/3068335

    Article  MathSciNet  Google Scholar 

  18. Barros J, Araujo M, Rossetti RJF (2015) Short-term real-time traffic prediction methods: A survey. In: 2015 International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS), Budapest, Hungary, pp 132–139. https://doi.org/10.1109/MTITS.2015.7223248

  19. Smith BL, Demetsky MJ (1997) Traffic flow forecasting: comparison of modeling approaches. J Transp Eng 123:261–266

    Article  Google Scholar 

  20. Emami A, Sarvi M, Asadi Bagloee S (2019) Using Kalman filter algorithm for short-term traffic flow prediction in a connected vehicle environment. J Mod Transp 27:222–232. https://doi.org/10.1007/s40534-019-0193-2

    Article  Google Scholar 

  21. Lin X, Huang Y (2021) Short-term high-speed traffic flow prediction based on ARIMA-GARCH-M model. Wireless Pers Commun 117:3421–3430. https://doi.org/10.1007/s11277-021-08085-z

    Article  Google Scholar 

  22. Comert G, Begashaw N, Huynh N (2021) Improved grey system models for predicting traffic parameters. Expert Syst Appl 177:1–11. https://doi.org/10.1016/j.eswa.2021.114972

    Article  Google Scholar 

  23. Zheng L, Yang J, Chen L et al (2020) Dynamic spatial-temporal feature optimization with ERI big data for short-term traffic flow prediction. Neurocomputing 412:339–350. https://doi.org/10.1016/j.neucom.2020.05.038

    Article  Google Scholar 

  24. Cheng Z, Lu J, Zhou H et al (2022) Short-term traffic flow prediction: an integrated method of econometrics and hybrid deep learning. IEEE Trans Intell Transp Syst 23:5231–5244. https://doi.org/10.1109/TITS.2021.3052796

    Article  Google Scholar 

  25. Zhang W, Yu Y, Qi Y et al (2019) Short-term traffic flow prediction based on spatio-temporal analysis and CNN deep learning. Transportmetrica A: Transp Sci 15:1688–1711. https://doi.org/10.1080/23249935.2019.1637966

    Article  Google Scholar 

  26. Miglani A, Kumar N (2019) Deep learning models for traffic flow prediction in autonomous vehicles: a review, solutions, and challenges. Veh Commun 20:1–36. https://doi.org/10.1016/j.vehcom.2019.100184

    Article  Google Scholar 

  27. Chen D (2017) Research on traffic flow prediction in the big data environment based on the improved RBF neural network. IEEE Trans Industr Inf 13:2000–2008. https://doi.org/10.1109/TII.2017.2682855

    Article  Google Scholar 

  28. Xu M, Fang J, Tong Y (2022) An intelligent adaptive spatiotemporal graph approach for GPS-data-based travel-time estimation. IEEE Intell Transp Syst Mag 14:222–237. https://doi.org/10.1109/MITS.2021.3099796

    Article  Google Scholar 

  29. Huang W, Song G, Hong H, Xie K (2014) Deep architecture for traffic flow prediction: deep belief networks with multitask learning. IEEE Trans Intell Transp Syst 15:2191–2201. https://doi.org/10.1109/TITS.2014.2311123

    Article  Google Scholar 

  30. Sun RY (2020) Optimization for deep learning: an overview. J Oper Res Soc China 8:249–294. https://doi.org/10.1007/s40305-020-00309-6

    Article  MathSciNet  Google Scholar 

  31. He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: CVPR(2016). IEEE, Las Vegas,USA, pp. 770–778. https://doi.org/10.1109/CVPR.2016.90

  32. Wu Y, Tan H, Qin L et al (2018) A hybrid deep learning based traffic flow prediction method and its understanding. Transp Res Part C: Emerg Technol 90:166–180. https://doi.org/10.1016/j.trc.2018.03.001

    Article  Google Scholar 

  33. Guo S, Lin Y, Li S et al (2019) Deep spatial-temporal 3D convolutional neural networks for traffic data forecasting. IEEE Trans Intell Transp Syst 20:3913–3926. https://doi.org/10.1109/TITS.2019.2906365

    Article  Google Scholar 

  34. Li W, Wang X, Zhang Y, Wu Q (2021) Traffic flow prediction over muti-sensor data correlation with graph convolution network. Neurocomputing 427:50–63. https://doi.org/10.1016/j.neucom.2020.11.032

    Article  Google Scholar 

  35. Han SY, Zhao Q, Sun QW et al (2022) EnGS-DGR: traffic flow forecasting with indefinite forecasting interval by ensemble GCN, Seq2Seq, and dynamic graph reconfiguration. Appl Sci (Switzerland) 12:1–14. https://doi.org/10.3390/app12062890

    Article  Google Scholar 

  36. Zhao L, Song Y, Zhang C et al (2020) T-GCN: a temporal graph convolutional network for traffic prediction. IEEE Trans Intell Transp Syst 21:3848–3858. https://doi.org/10.1109/TITS.2019.2935152

    Article  Google Scholar 

  37. Yu B, Yin H, Zhu Z (2018) Spatio-temporal graph convolutional networks: a deep learning framework for traffic forecasting. In: IJCAI(2018). IJCAI, Stockholm, Sweden, pp 3634–3640. https://doi.org/10.24963/ijcai.2018/505

  38. Li Y, Yu R, Shahabi C, Liu Y (2018) Diffusion convolutional recurrent neural network: data-driven traffic forecasting. In: ICLR(2018). Vancouver, Canada, pp 1–16. https://doi.org/10.48550/arXiv.1707.01926

  39. Guo S, Lin Y, Feng N et al (2019) Attention based spatial-temporal graph convolutional networks for traffic flow forecasting. AAAI(2019). AAAI, Hawaii, USA, pp. 922–929. https://doi.org/10.1609/aaai.v33i01.3301922

  40. Song C, Lin Y, Guo S, Wan H (2020) Spatial-temporal synchronous graph convolutional networks: a new framework for spatial-temporal network data forecasting. AAAI(2020). AAAI, Palo Alto, USA, pp 914–921. https://doi.org/10.1609/aaai.v34i01.5438

  41. Xing H, Chen A, Zhang X (2023) RL-GCN: traffic flow prediction based on graph convolution and reinforcement learning for smart cities. Displays 80:102513. https://doi.org/10.1016/j.displa.2023.102513

    Article  Google Scholar 

  42. Peng H, Du B, Liu M et al (2021) Dynamic graph convolutional network for long-term traffic flow prediction with reinforcement learning. Inf Sci 578:401–416. https://doi.org/10.1016/j.ins.2021.07.007

    Article  MathSciNet  Google Scholar 

  43. Li B, Li Z, Chen J et al (2024) MAST-GNN: a multimodal adaptive spatio-temporal graph neural network for airspace complexity prediction. Transp Res Part C: Emerg Technol 160:104521. https://doi.org/10.1016/j.trc.2024.104521

    Article  Google Scholar 

  44. Huang X, Wang J, Lan Y et al (2023) MD-GCN: a multi-scale temporal dual graph convolution network for traffic flow prediction. Sensors 23:841. https://doi.org/10.3390/s23020841

    Article  Google Scholar 

  45. Li M, Zhu Z (2021) Spatial-temporal fusion graph neural networks for traffic flow forecasting. In: AAAI(2021). Online, pp 4189–4196. https://doi.org/10.1609/aaai.v35i5.16542

  46. Jin G, Li F, Zhang J et al (2022) Automated dilated spatio-temporal synchronous graph modeling for traffic prediction. IEEE Trans Intell Transp Syst 1–11:1. https://doi.org/10.1109/TITS.2022.3195232

    Article  Google Scholar 

  47. Lin G, Wu Q, Qiu L, Huang X (2018) Image super-resolution using a dilated convolutional neural network. Neurocomputing 275:1219–1230. https://doi.org/10.1016/j.neucom.2017.09.062

    Article  Google Scholar 

  48. Benesty J, Chen J, Huang Y, Cohen I (2009) Pearson correlation coefficient. In: Noise Reduction in Speech Processing. Springer Topics in Signal Processing, vol 2. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-00296-0_5

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

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by the National Natural Science Foundation of China (62476145); the Humanity and Social Science Foundation of Ministry of Education of China (24YJAZH126); the 6th “333 Talents” Technology Research and Development Talent Foundation of Jiangsu Province; the Transportation Technology and Achievement Transformation Foundation of Jiangsu Province (2024G01).

Author information

Authors and Affiliations

  1. School of Information Science and Technology, Nantong University, Nantong, China

    Yinxin Bao, Yang Cao & Quan Shi

  2. School of Transportation and Civil Engineering, Nantong University, Nantong, China

    Qinqin Shen, Yang Cao & Quan Shi

Authors
  1. Yinxin Bao
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Qinqin Shen
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Yang Cao
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Quan Shi
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Quan Shi.

Ethics declarations

Conflict of interest

The authors declare that they have no conflicts of interest in this work.

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

Bao, Y., Shen, Q., Cao, Y. et al. Spatial-temporal clustering enhanced multi-graph convolutional network for traffic flow prediction. Appl Intell 55, 445 (2025). https://doi.org/10.1007/s10489-025-06329-0

Download citation

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10489-025-06329-0

Keywords