Skip to main content
SpringerLink
Log in

Spatial-temporal graph neural network based on gated convolution and topological attention for traffic flow prediction

  • Published:
Applied Intelligence Aims and scope Submit manuscript

Abstract

Accurate traffic flow prediction is essential for developing intelligent transportation systems (ITS) and providing real-time traffic applications. This study proposes a novel Spatial-Temporal Graph Neural Network based on Gated Convolution and Topological Attention (STGNN-GCTA) to accurately model complex spatiotemporal traffic flow correlations. In the temporal dimension, we design a novel Gated-Memory Convolutional Neural Network (GMCNN) to capture the non-linear temporal dependencies by controlling the output based on the timing information position. In the spatial dimension, we develop a Multilayer Graph Topological Attention Network (MGTAN) to capture the dynamic spatial dependencies by identifying high-impact neighborhood segments in each time step. In particular, we improve the model’s prediction robustness in a noisy environment using the Network Smoothing Training (NST) method. Experimental results on two public traffic datasets demonstrate that STGNN-GCTA has higher prediction accuracy and execution efficiency than baseline methods and exhibits excellent robustness.

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

Access this article

Log in via an institution

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
Algorithm 1
Algorithm 2
Fig. 6
Fig. 7
Algorithm 3
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15

Similar content being viewed by others

Data Availability

The datasets generated during and analyzed during the current study are available from the corresponding author on reasonable request.

References

  1. Kaffash S, Nguyen AT, Zhu J (2021) Big data algorithms and applications in intelligent transportation system: A review and bibliometric analysis. Int J Prod Econ 231:107868

    Google Scholar 

  2. McGowan CJ, Biggerstaff M, Johansson M, Apfeldorf KM, Ben-Nun M, Brooks L, Convertino M, Erraguntla M, Farrow DC, Freeze J et al (2019) Collaborative efforts to forecast seasonal influenza in the united states, 2015–2016. Sci Rep 9(1):683

    Google Scholar 

  3. Jamil R (2020) Hydroelectricity consumption forecast for pakistan using ARIMA modeling and supply-demand analysis for the year 2030. Renew Energy 154:1–10

    Google Scholar 

  4. Boukerche A, Wang J (2020) Machine learning-based traffic prediction models for intelligent transportation systems. Comput Netw 181:107530

    Google Scholar 

  5. Wang S, Cao J, Philip SY (2022) Deep learning for spatio-temporal data mining: A survey. IEEE Trans Knowl Data Eng 34(08):3681–3700

    Google Scholar 

  6. Assaf AG, Li G, Song H, Tsionas MG (2019) Modeling and forecasting regional tourism demand using the bayesian global vector autoregressive (BGVAR) model. J Travel Res 58(3):383–397

  7. Xia D, Wang B, Li H, Li Y, Zhang Z (2016) A distributed spatial-temporal weighted model on mapreduce for short-term traffic flow forecasting. Neurocomputing 179:246–263

    Google Scholar 

  8. Tang J, Chen X, Hu Z, Zong F, Han C, Li L (2019) Traffic flow prediction based on combination of support vector machine and data denoising schemes. Phys A Stat Mech Appl 534:120642

    Google Scholar 

  9. Liu X, Zhu X, Li M, Wang L, Zhu E, Liu T, Kloft M, Shen D, Yin J, Gao W (2020) Multiple kernel \(k\)-means with incomplete kernels. IEEE Trans Patt Anal Mach Intell 42(5):1191–1204

    Google Scholar 

  10. Yu X, Ye X, Zhang S (2022) Floating pollutant image target extraction algorithm based on immune extremum region. Digit Sig Process 123:103442

    Google Scholar 

  11. Zhou Z, Zhang B, Yu X (2022) Immune coordination deep network for hand heat trace extraction. Infrared Phys Technol 127:104400

    Google Scholar 

  12. Sherstinsky A (2020) Fundamentals of recurrent neural network (RNN) and long short-term memory (LSTM) network. Physica D: Nonlinear Phenomena 404:132306

    MathSciNet  Google Scholar 

  13. Cho K, Van Merriënboer B, Gulcehre C, Bahdanau D, Bougares F, Schwenk H, Bengio Y (2014) Learning phrase representations using RNN encoder-decoder for statistical machine translation. In: Proc. of EMNLP, pp 1724–1734

  14. Ma X, Tao Z, Wang Y, Yu H, Wang Y (2015) Long short-term memory neural network for traffic speed prediction using remote microwave sensor data. Transport Res Part C Emerg Tech 54:187–197

    Google Scholar 

  15. Sutskever I, Vinyals O, Le VQ (2014) Sequence to sequence learning with neural networks. Adv Neural Inf Process Syst 27:3104–3112

    Google Scholar 

  16. Yang D, Chen K, Yang M, Zhao X (2019) Urban rail transit passenger flow forecast based on LSTM with enhanced long-term features. IET Intell Transp Syst 13(10):1475–1482

    Google Scholar 

  17. Xia D, Zhang M, Yan X, Bai Y, Zheng Y, Li Y, Li H (2021) A distributed WND-LSTM model on mapreduce for short-term traffic flow prediction. Neural Comput & Applic 33(7):2393–2410

  18. Chu K-F, Lam AY, Li VO (2019) Deep multi-scale convolutional LSTM network for travel demand and origin-destination predictions. IEEE Trans Intell Transp Syst 21(8):3219–3232

    Google Scholar 

  19. Bengio Y, Simard P, Frasconi P (1994) Learning long-term dependencies with gradient descent is difficult. IEEE Trans Neural Netw 5(2):157–166

    Google Scholar 

  20. Bai S, Kolter JZ, Koltun V (2018) Convolutional sequence modeling revisited. In: Proc. of ICLR, pp 1–20

  21. Lea C, Flynn MD, Vidal R, Reiter A, Hager GD (2017) Temporal convolutional networks for action segmentation and detection. In: Proc. of CVPR, pp 156–165

  22. Yu F, Koltun V, Funkhouser TA (2017) Dilated residual networks. In: Proc. of CVPR, pp 636–644

  23. Yao H, Tang X, Wei H, Zheng G, Li Z (2019) Revisiting spatial-temporal similarity: A deep learning framework for traffic prediction. Proc. of AAAI 33:5668–5675

    Google Scholar 

  24. Liu Y, Yu JJQ, Kang J, Niyato D, Zhang S (2020) Privacy-preserving traffic flow prediction: A federated learning approach. IEEE Internet Things J 7(8):7751–7763

    Google Scholar 

  25. Zhou J, Cui G, Hu S, Zhang Z, Yang C, Liu Z, Wang L, Li C, Sun M (2020) Graph neural networks: A review of methods and applications. AI Open 1:57–81

    Google Scholar 

  26. Jiang Z (2018) A survey on spatial prediction methods. IEEE Trans Knowl Data Eng 31(9):1645–1664

    Google Scholar 

  27. Veličković P, Cucurull G, Casanova A, Romero A, Liò P, Bengio Y (2018) Graph attention networks. In: Proc. of ICLR, pp 1–12

  28. Anava O, Hazan E, Zeevi A (2015) Online time series prediction with missing data. In: Proc. of ICML, pp 2191–2199

  29. Zhang J, Wang F-Y, Wang K, Lin W-H, Xu X, Chen C (2011) Data-driven intelligent transportation systems: A survey. IEEE Trans Intell Transport Syst 12(4):1624–1639

    Google Scholar 

  30. Liu J, Ong GP, Chen X (2020) Graphsage-based traffic speed forecasting for segment network with sparse data. IEEE Trans Intell Transport Syst 23(3):1755–1766

    Google Scholar 

  31. Li Y, Zheng Y (2019) Citywide bike usage prediction in a bike-sharing system. IEEE Trans Knowl Data Eng 32(6):1079–1091

    Google Scholar 

  32. Zhang J, Zheng Y, Sun J, Qi D (2019) Flow prediction in spatio-temporal networks based on multitask deep learning. IEEE Trans Knowl Data Eng 32(3):468–478

    Google Scholar 

  33. Mulder WD, Bethard S, Moens MF (2015) A survey on the application of recurrent neural networks to statistical language modeling. Comp Speech Lang 30(1):61–98

    Google Scholar 

  34. Dauphin YN, Fan A, Auli M, Grangier D (2017) Language modeling with gated convolutional networks. In: Proc. of ICML, pp 933–941

  35. Landi F, Baraldi L, Cornia M, Cucchiara R (2021) Working memory connections for LSTM. Neural Networks 144:334–341

    Google Scholar 

  36. Huang X, Ye Y, Wang C, Yang X, Xiong L (2022) A multi-mode traffic flow prediction method with clustering based attention convolution LSTM. Appl Intell 52(13):14773–14786

    Google Scholar 

  37. Davis N, Raina G, Jagannathan K (2020) Grids versus graphs: Partitioning space for improved taxi demand-supply forecasts. IEEE Trans Intell Transport Sys 22(10):6526–6535

    Google Scholar 

  38. Yusuf AA, Chong F, Xianling M (2022) An analysis of graph convolutional networks and recent datasets for visual question answering. Artif Intell Rev 55(8):6277–6300

    Google Scholar 

  39. Yu B, Yin H, Zhu Z (2018) Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. In: Proc. of IJCAI, pp 3634–3640

  40. Li Y, Yu R, Shahabi C, Liu Y (2018) Diffusion convolutional recurrent neural network: Data-driven traffic forecasting. In: Proc. of ICLR, pp 1–16

  41. Wu Z, Pan S, Long G, Jiang J, Zhang C (2019) Graph wavenet for deep spatial-temporal graph modeling. In: Proc. of IJCAI, pp 1907–1913

  42. Zhang J, Chen F, Guo Y, Li X (2020) Multi-graph convolutional network for short-term passenger flow forecasting in urban rail transit. IET Intell Trans Sys 14(10):1210–1217

    Google Scholar 

  43. Zhao L, Song Y, Zhang C, Liu Y, Wang P, Lin T, Deng M, Li H (2020) T-GCN: A temporal graph convolutional network for traffic prediction. IEEE Trans Intell Transport Syst 21(9):3848–3858

    Google Scholar 

  44. Kong X, Zhang J, Wei X, Xing W, Lu W (2022) Adaptive spatial-temporal graph attention networks for traffic flow forecasting. Appl Intell 52(4):4300–4316

    Google Scholar 

  45. Qi T, Li G, Chen L, Xue Y (2022) ADGCN: An asynchronous dilation graph convolutional network for traffic flow prediction. IEEE Internet Things J 9(5):4001–4014

    Google Scholar 

  46. Zhou F, Yang Q, Zhong T, Chen D, Zhang N (2021) Variational graph neural networks for road traffic prediction in intelligent transportation systems. IEEE Trans Ind Inf 17(4):2802–2812

    Google Scholar 

  47. Luo D, Zhao D, Ke Q, You X, Liu L, Ma H (2022) Spatiotemporal hashing multigraph convolutional network for service-level passenger flow forecasting in bus transit systems. IEEE Internet Things J 9(9):6803–6815

    Google Scholar 

  48. Huang X, Ye Y, Ding W, Yang X, Xiong L (2022) Multi-mode dynamic residual graph convolution network for traffic flow prediction. Inf Sci 609:548–564

    Google Scholar 

  49. Cao S, Wu L, Wu J, Wu D, Li Q (2022) A spatio-temporal sequence-to-sequence network for traffic flow prediction. Inf Sci 610:185–203

    Google Scholar 

  50. Do LN, Vu HL, Vo BQ, Liu Z, Phung D (2019) An effective spatial-temporal attention based neural network for traffic flow prediction. Transport Res Part C Emerg Technol 108:12–28

    Google Scholar 

  51. Guo S, Lin Y, Feng N, Song C, Wan H (2019) Attention based spatial-temporal graph convolutional networks for traffic flow forecasting. Proc. of AAAI 33:922–929

    Google Scholar 

  52. Pan Z, Liang Y, Wang W, Yu Y, Zheng Y, Zhang J (2019) Urban traffic prediction from spatio-temporal data using deep meta learning. In: Proc. of KDD, p 1720–1730

  53. Zheng C, Fan X, Wang C, Qi J (2020) GMAN: A graph multi-attention network for traffic prediction. Proc. of AAAI 34:1234–1241

    Google Scholar 

  54. Wang Y, Jing C, Xu S, Guo T (2022) Attention based spatiotemporal graph attention networks for traffic flow forecasting. Inf Sci 607:869–883

    Google Scholar 

  55. Zhang X, Huang C, Xu Y, Xia L, Dai P, Bo L, Zhang J, Zheng Y (2021) Traffic flow forecasting with spatial-temporal graph diffusion network. Proc. of AAAI 35:15008–15015

    Google Scholar 

  56. Lu B, Gan X, Jin H, Fu L, Wang X, Zhang H (2022) Make more connections: Urban traffic flow forecasting with spatiotemporal adaptive gated graph convolution network. ACM Trans Intell Syst Tech 13(2):1–25

    Google Scholar 

  57. He K, Zhang X, Ren S, Sun J (2015) Delving deep into rectifiers: Surpassing human-level performance on imagenet classification. In: Proc. of ICCV, pp 1026–1034

  58. Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser L, Polosukhin I (2017) Attention is all you need. In: Proc. of NeurIPS, pp 5998–6008

  59. Liu S, Zhang X, Xu L, Ding F (2022) Expectation-maximization algorithm for bilinear systems by using the Rauch-Rung-Striebel smoother. Automatica 142:110365

    Google Scholar 

  60. Chiang K-W, Tsai G-J, Chang H, Joly C, Ei-Sheimy N (2019) Seamless navigation and mapping using an INS/GNNS/grid-based slam semi-tightly coupled integration scheme. Inf Fusion 50:181–196

    Google Scholar 

  61. Liu H, Nassar S, El-Sheimy N (2010) Two-filter smoothing for accurate INS/GPS land-vehicle navigation in urban centers. IEEE Trans Veh Technol 59(9):4256–4267

    Google Scholar 

  62. Seo T (2020) Calibration-free traffic state estimation method using single detector and connected vehicles with kalman filtering and RTS smoothing. In: Proc. of ITSC, pp 1–5

  63. Chen C, Petty K, Skabardonis A, Varaiya P, Jia Z (2001) Freeway performance measurement system: Mining loop detector data. Transport Res Rec 1748(1):96–102

    Google Scholar 

  64. Loshchilov I, Hutter F (2019) Decoupled weight decay regularization. In: Proc. of ICLR, pp 1–8

Download references

Acknowledgements

This work was supported in part by the National Natural Science Foundation of China (Grant nos. 62162012, 62173278, and 62072061), the High-Level Innovative Talent Project of Guizhou Province (Grant no. QKHPTRC-GCC2023027), the Science and Technology Support Program of Guizhou Province (Grant no. QKHZC2021YB531), the Natural Science Research Project of Department of Education of Guizhou Province (Grant nos. QJJ2022015, QJJ2022047, QJJ2023012, and QJJ2023061), the Science and Technology Foundation of Guizhou Province (Grant nos. QKHJCZK2022YB197 and QKHJCZK2023YB143), the Youth Science and Technology Talents Development Project of Guizhou (Grant no. QJHKY2022175), and the Scientific Research Platform Project of Guizhou Minzu University (Grant no. GZMUSYS202104).

Author information

Authors and Affiliations

  1. College of Data Science and Information Engineering, Guizhou Minzu University, Guiyang, China

    Dewei Bai, Dawen Xia & Dan Huang

  2. Department of Automotive Engineering, Guizhou Traffic Technician and Transportation College, Guiyang, China

    Yang Hu

  3. College of Computer Science, Chongqing University, Chongqing, China

    Yantao Li

  4. College of Electronic and Information Engineering, Southwest University, Chongqing, China

    Huaqing Li

Authors
  1. Dewei Bai
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Dawen Xia
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Dan Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yang Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yantao Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Huaqing Li
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Dawen Xia or Huaqing Li.

Ethics declarations

Competing 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

Bai, D., Xia, D., Huang, D. et al. Spatial-temporal graph neural network based on gated convolution and topological attention for traffic flow prediction. Appl Intell 53, 30843–30864 (2023). https://doi.org/10.1007/s10489-023-05053-x

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10489-023-05053-x

Keywords

Search

Navigation