PSTCGCN: Principal spatio-temporal causal graph convolutional network for traffic flow prediction (original) (raw)

Abstract

Traffic prediction is indispensable for constructing transportation networks in smart cities. Due to the complex spatio-temporal correlations of traffic data, this task presents challenges. Recent studies mainly use graph neural networks to simulate complex spatio-temporal relationships through fixed or adaptive graphs. While fixed graphs may not adapt to data drift caused by changes in road network structures, adaptive graphs overlook critical information of the original roads. To address this challenge, we propose a principal spatio-temporal causal graph convolutional network (PSTCGCN) to accurately predict traffic flow. In response to the data drift problem, we introduce a data-driven semi-principal generated graph embedding (SPGGE) that first extracts the principal features of the original roads to model the spatio-temporal sequence distribution and then remodels the data after drift through adaptive transformation. Traffic flow data, while showcasing fundamental spatial relationships, also exhibit temporal dynamics. We propose an effective temporal causal convolution component comprising SPGGE, graph convolution, both local and global causal learning models to jointly learn short-term and long-term spatio-temporal correlations. PSTCGCN was evaluated using two actual highway datasets, PEMS03 and PEMS07, achieving a notable improvement of 6.12% in RMSE on PEMS03 compared to STGATRGN. Our code is available at https://github.com/OvOYu/PSTCGCN.

Access this article

Log in via an institution

Subscribe and save

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

Data availability

References

  1. Sayed SA, Abdel-Hamid Y, Hefny HA (2023) Artificial intelligence-based traffic flow prediction: a comprehensive review. J Electr Syst Inf Technol 10(1):13
    Article Google Scholar
  2. Jiang W, Luo J, He M et al (2023) Graph neural network for traffic forecasting: the research progress. ISPRS Int J Geo-Inf 12(3):100
    Article Google Scholar
  3. Wang Y, Ke S, An C et al (2024) A hybrid framework combining LSTM NN and BNN for short-term traffic flow prediction and uncertainty quantification. KSCE J Civ Eng 28(1):363–374
    Article Google Scholar
  4. Han Y, Zhao S, Deng H et al (2023) Principal graph embedding convolutional recurrent network for traffic flow prediction. Appl Intell 53(14):17809–17823
    Article Google Scholar
  5. Soni R, Roy P, Nagwanshi KK (2024) WKNN-FDCNN method for big data driven traffic flow prediction in its. Multimed Tools Appl 83(9):25261–25286
    Article Google Scholar
  6. 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. AAAI Press, pp 3634–3640
  7. Wu Z, Pan S, Long G, et al (2019) Graph wavenet for deep spatial-temporal graph modeling. In: Proceedings of international joint conference on artificial intelligence. AAAI Press, pp 1907–1913
  8. Bai L, Yao L, Li C, et al (2020) Adaptive graph convolutional recurrent network for traffic forecasting. In: Advances in neural information processing systems. MIT Press, pp 17804–17815
  9. Jiang W, Luo J (2022) Graph neural network for traffic forecasting: a survey. Expert Syst Appl 207:117921
    Article Google Scholar
  10. Lin Z, Feng J, Lu Z, et al (2019) Deepstn+: context-aware spatial-temporal neural network for crowd flow prediction in metropolis. In: Proceedings of the AAAI conference on artificial intelligence, pp 1020–1027
  11. Geng X, Li Y, Wang L, et al (2019) Spatiotemporal multi-graph convolution network for ride-hailing demand forecasting. In: Proceedings of the AAAI conference on artificial intelligence, pp 3656–3663
  12. Sha S, Li J, Zhang K et al (2020) RNN-based subway passenger flow rolling prediction. IEEE Access 8:15232–15240
    Article Google Scholar
  13. Karimzadeh M, Aebi R, de Souza AM, et al (2021) Reinforcement learning-designed LSTM for trajectory and traffic flow prediction. In: 2021 IEEE wireless communications and networking conference (WCNC), IEEE, pp 1–6
  14. Sun P, Boukerche A, Tao Y (2020) SSGRU: a novel hybrid stacked GRU-based traffic volume prediction approach in a road network. Comput Commun 160:502–511
    Article Google Scholar
  15. Zhao W, Zhang S, Wang B et al (2023) Spatio-temporal causal graph attention network for traffic flow prediction in intelligent transportation systems. PeerJ Comput Sci 9:e1484
    Article Google Scholar
  16. Liu A, Zhang Y (2024) Spatial–temporal dynamic graph convolutional network with interactive learning for traffic forecasting. IEEE Trans Intell Transp Syst 1–16
  17. Zheng H, Lin F, Feng X et al (2020) A hybrid deep learning model with attention-based conv-LSTM networks for short-term traffic flow prediction. IEEE Trans Intell Transp Syst 22(11):6910–6920
    Article Google Scholar
  18. Ma D, Song X, Li P (2020) Daily traffic flow forecasting through a contextual convolutional recurrent neural network modeling inter-and intra-day traffic patterns. IEEE Trans Intell Transp Syst 22(5):2627–2636
    Article Google Scholar
  19. 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. AAAI Press, pp 4189–4196
  20. Kipf TN, Welling M (2016) Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907
  21. Wu Z, Pan S, Chen F et al (2020) A comprehensive survey on graph neural networks. IEEE Trans Neural Netw Learn Syst 32(1):4–24
    Article MathSciNet Google Scholar
  22. Monti F, Boscaini D, Masci J et al (2017) Geometric deep learning on graphs and manifolds using mixture model CNNs. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 5115–5124
  23. Li R, Wang S, Zhu F, et al (2018) Adaptive graph convolutional neural networks. In: Proceedings of the AAAI conference on artificial intelligence, pp 3546–3553
  24. Zhang J, Shi X, Xie J, et al (2018) Gaan: Gated attention networks for learning on large and spatiotemporal graphs. arXiv preprint arXiv:1803.07294
  25. Duan Y, Lv Y, Liu YL et al (2016) An efficient realization of deep learning for traffic data imputation. Transp Res Part C: Emerg Technol 72:168–181
    Article Google Scholar
  26. Dissanayake B, Hemachandra O, Lakshitha N et al (2021) A comparison of arimax, var and lstm on multivariate short-term traffic volume forecasting. In: Conference of open innovations association, FRUCT, FRUCT Oy, vol 28, pp 564–570
  27. Zhang Y, Xu S, Zhang L, et al (2024) Short-term multi-step-ahead sector-based traffic flow prediction based on the attention-enhanced graph convolutional LSTM network (AGC-LSTM). Neural Comput Appl
  28. Fu J, Zhou W, Chen Z (2024) Bayesian graph convolutional network for traffic prediction. Neurocomputing 582:127507
    Article Google Scholar
  29. Li Y, Shao Z, Xu Y et al (2024) Dynamic frequency domain graph convolutional network for traffic forecasting. ICASSP 2024–2024 IEEE international conference on acoustics, speech and signal processing (ICASSP), IEEE, pp 5245–5249
  30. Vaswani A, Shazeer N, Parmar N, et al (2017) Attention is all you need. In: Advances in neural information processing systems 30
  31. Lan S, Ma Y, Huang W, et al (2022) DSTAGNN: dynamic spatial-temporal aware graph neural network for traffic flow forecasting. In: International conference on machine learning, PMLR, pp 11906–11917
  32. Wu D, Peng K, Wang S et al (2024) Spatial-temporal graph attention gated recurrent transformer network for traffic flow forecasting. IEEE Internet Things J 11(8):14267–14281
    Article Google Scholar
  33. Li T, Ni A, Zhang C et al (2020) Short-term traffic congestion prediction with conv-BiLSTM considering spatio-temporal features. IET Intell Transp Syst 14(14):1978–1986
    Article Google Scholar
  34. Li Y, Yu R, Shahabi C, et al (2018) Diffusion convolutional recurrent neural network: data-driven traffic forecasting. In: International conference on learning representations
  35. Guo S, Lin Y, Feng N, et al (2019) Attention based spatial-temporal graph convolutional networks for traffic flow forecasting. In: Proceedings of the AAAI conference on artificial intelligence. AAAI Press, pp 922–929
  36. Song C, Lin Y, Guo S, et al (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. AAAI Press, pp 914–921
  37. Fang Z, Long Q, Song G et al (2021) Spatial-temporal graph ODE networks for traffic flow forecasting. In: Proceedings of the ACM SIGKDD international conference on knowledge discovery & data mining. Association for Computing Machinery, New York, NY, USA, pp 364–373
  38. Bao Y, Huang J, Shen Q et al (2023) Spatial-temporal complex graph convolution network for traffic flow prediction. Eng Appl Artif Intell 121:106044
    Article Google Scholar
  39. Liu X, Liang Y, Huang C et al (2023) Do we really need graph neural networks for traffic forecasting? arXiv preprint arXiv:2301.12603
  40. Greenacre M, Groenen PJ, Hastie T et al (2022) Principal component analysis. Nat Rev Methods Primers 2(1):100
    Article Google Scholar
  41. Bharadiya JP (2023) A tutorial on principal component analysis for dimensionality reduction in machine learning. Int J Innov Sci Res Technol 8(5):2028–2032
    Google Scholar
  42. Jia W, Sun M, Lian J et al (2022) Feature dimensionality reduction: a review. Complex Intell Syst 8(3):2663–2693
    Article Google Scholar
  43. Naeem S, Ali A, Anam S et al (2023) An unsupervised machine learning algorithms: comprehensive review. Int J Comput Dig Syst
  44. Bai S, Kolter JZ, Koltun V (2018) An empirical evaluation of generic convolutional and recurrent networks for sequence modeling. In: International conference on learning representations workshop
  45. Williams BM, Hoel LA (2003) Modeling and forecasting vehicular traffic flow as a seasonal ARIMA process: theoretical basis and empirical results. J Transp Eng 129(6):664–672
    Article Google Scholar
  46. Shi X, Chen Z, Wang H et al (2015) Convolutional LSTM network: a machine learning approach for precipitation nowcasting. In: Advances in neural information processing systems. MIT Press, pp 802–810
  47. Bai L, Yao L, Kanhere SS, et al (2019) Stg2seq: spatial-temporal graph to sequence model for multi-step passenger demand forecasting. In: Proceedings of the international joint conference on artificial intelligence. AAAI Press, pp 1981–1987
  48. Huang R, Huang C, Liu Y et al (2020) LSGCN: long short-term traffic prediction with graph convolutional networks. In: Proceedings of the international joint conference on artificial intelligence. AAAI Press, pp 2355–2361
  49. Cao D, Wang Y, Duan J et al (2020) Spectral temporal graph neural network for multivariate time-series forecasting. Adv Neural Inf Process Syst 33:17766–17778
    Google Scholar
  50. Chen Y, Segovia I, Gel YR (2021) Z-GCNETs: Time zigzags at graph convolutional networks for time series forecasting. In: International conference on machine learning, PMLR, pp 1684–1694
  51. Chen Y, Segovia-Dominguez I, Coskunuzer B, et al (2022) Tamp-s2gcnets: coupling time-aware multipersistence knowledge representation with spatio-supra graph convolutional networks for time-series forecasting. In: International conference on learning representations

Download references

Funding

This study was funded by the National Nature Science Foundation of China (Grant number No. 41471333) and National Nature Science Foundation of China (Grant number No. 42201500)

Author information

Authors and Affiliations

  1. Academy of Digital China (Fujian), Fuzhou University, Fuzhou, 350108, China
    Shiyu Yang, Qunyong Wu, Ziwei Li & Keyue Wang
  2. Key Laboratory of Spatial Data Mining and Information Sharing of Ministry of Education, Fuzhou University, Fuzhou, 350108, China
    Shiyu Yang, Qunyong Wu, Ziwei Li & Keyue Wang
  3. National Engineering Research Centre of Satellite Geospatial Information Technology, Fuzhou University, Fuzhou, 350108, China
    Shiyu Yang, Qunyong Wu, Ziwei Li & Keyue Wang

Authors

  1. Shiyu Yang
  2. Qunyong Wu
  3. Ziwei Li
  4. Keyue Wang

Contributions

Conceptualization was contributed by Shiyu Yang and Qunyong Wu; methodology, vizualization, and writing–original draft preparation were involved by Shiyu Yang; writing–review and editing was done by Qunyong Wu, Ziwei Li, and Keyue Wang; All authors have read and agreed to the published version of the manuscript.

Corresponding author

Correspondence toQunyong Wu.

Ethics declarations

Conflict 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

Cite this article

Yang, S., Wu, Q., Li, Z. et al. PSTCGCN: Principal spatio-temporal causal graph convolutional network for traffic flow prediction.Neural Comput & Applic 37, 14751–14764 (2025). https://doi.org/10.1007/s00521-024-10591-7

Download citation

Keywords