Skip to main content
SpringerLink
Log in

A multi-mode traffic flow prediction method with clustering based attention convolution LSTM

  • Published:
Applied Intelligence Aims and scope Submit manuscript

Abstract

Increasing traffic congestion is a major obstacle to the development of cities. The prediction of traffic flow is very important to city planning and dredging. A good model of flow is able to accurately predict future flow by learning historical flow data. Traffic flow is usually affected by macro and micro factors. At the macro level, the whole city can be divided into different subregions according to the similarity in the traffic flow patterns. At the micro-level, there is a temporal and spatial correlation between the traffic flow of different road sections at di fferent times. In this paper, we propose a multi-mode traffic flow prediction method with Clustering based Attention Convolution LSTM (CACLSTM) to model spatial-temporal data of traffic flow. The framework includes three modules: a convolution LSTM encoding-decoding layer which is used to predict the traffic flow of the next time slice by encoding the historical traffic information, a clustering based attention layer which is able to extract different temporal features by clustering based attention, and an additional factors layer which can integrate weather, wind speed, holidays and other factors to improve the prediction accuracy. The experimental results on Beijing taxis data show that the CACLSTM method performs more effective than the six well-known compared methods.

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
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  1. Alghamdi T, Elgazzar K, Bayoumi M (2019) Forecasting traffic congestion using ARIMA modeling. In: Proceedings of the 15th international wireless communications and mobile computing conference, pp 1227–1232

  2. Amodei D, Ananthanarayanan S, Anubhai R (2016) Deep speech 2: End-to-end speech recognition in english and mandarin. In: Proceedings of the 33rd international conference on international conference on machine learning, pp 173–182

  3. Belhadi A, Djenouri Y, Djenouri D (2020) A recurrent neural network for urban long-term traffic flow forecasting. Appl Intell 50:3252–3265

    Article  Google Scholar 

  4. Ding C, Duan J, Zhang Y, Wu X, Yu G (2018) Using an ARIMA-GARCH modeling approach to improve subway short-term ridership forecasting accounting for dynamic volatility. IEEE Trans Intell Transp Syst 19(4):1054–1064

    Article  Google Scholar 

  5. Donahue J, Hendricks LA, Rohrbach ME (2017) Long-term recurrent convolutional networks for visual recognition and description. In: Proceedings of the 2015 IEEE conference on computer vision and pattern recognition, pp 677–691

  6. Guo S, Lin Y, Feng N (2019) Attention based spatial-temporal graph convolutional networks for traffic flow forecasting, pp 922–929

  7. Guo S, Lin Y, Li S, Chen Z, Wan H (2019) Deep spatial-temporal 3D convolutional neural networks for traffic data forecasting. IEEE Trans Intell Transp Syst 20(10):3913–3926

    Article  Google Scholar 

  8. He Z, Chow C, Zhang J (2019) Stann: A spatio-temporal attentive neural network for traffic prediction. IEEE Access 7:4795–4806

    Article  Google Scholar 

  9. Hoang MX, Yu Z (2016) SinghAmbujK: FCCF: forecasting citywide crowd flows based on big data. In: Proceedings of the 24th ACM SIGSPATIAL international conference on advances in geographic information systems, pp 1–10

  10. Huang R, Huang C, Liu Y (2020) LSGCN: Long short-term traffic prediction with graph convolutional networks. In: Proceedings of the 29th international joint conference on artificial intelligence and seventeenth pacific rim international conference on artificial intelligence, pp 2327–2333

  11. Jürgen S (2015) Deep learning in neural networks: An overview. Neural Netw 61:85–117

    Article  Google Scholar 

  12. Keyvan-Ekbatani M, Gao X, Gayah V, Knoop V (2019) Traffic-responsive signals combined with perimeter control: investigating the benefits. Transportmetrica B: Transport Dynamics 7(1):1402–1425

    Google Scholar 

  13. Khanmohammadi S, Adibeig N, Shanehbandy S (2017) An improved overlapping k-means clustering method for medical applications. Expert Syst Appl 67:12–18

    Article  Google Scholar 

  14. Kong X, Xing W, Wei X, Bao P, Zhang J, Lu W (2020) STGAT: Spatial-temporal graph attention networks for traffic flow forecasting. IEEE Access 8:134,363–134,372

    Article  Google Scholar 

  15. Li C, Cheung WK, Ye Y, Zhang X, Chu D, Li X (2015) The author-topic-community model for author interest profiling and community discovery. Knowledge & Information Systems 44(2):359–383

    Article  Google Scholar 

  16. Li C, Zhang H, Chu D, Xu X (2019) SRTM: a supervised relation topic model for multi-classification on large-scale document network. Neural Computing & Applications 32:6383–6392

    Article  Google Scholar 

  17. Liang Y, Ouyang K, Sun J, Wang Y, Zhang J, Zheng Y, Rosenblum D (2021) Fine-grained urban flow prediction. In: Proceedings of the 30th web conference, pp 1833–1845

  18. Liebig T, Piatkowski N, Bockermann C, Morik K (2017) Dynamic route planning with real-time traffic predictions. Inf Syst 64:258–265

    Article  Google Scholar 

  19. Liu B, Tang X, Cheng J (2020) Traffic flow combination forecasting method based on improved LSTM and ARIMA. Int J Embed Syst: 239–246

  20. Liu Y, Yang C, Huang K, Gui W (2020) Non-ferrous metals price forecasting based on variational mode decomposition and LSTM network. Knowl.-Based Syst 188(5):1–12

    Google Scholar 

  21. Luo X, Li D, Yang Y (2019) Spatiotemporal traffic flow prediction with KNN and LSTM. J Adv Transp 2019(5):537–546

    Google Scholar 

  22. Lv Y, Duan Y, Kang W (2015) Traffic flow prediction with big data: a deep learning approach. IEEE Trans Intell Transp Syst 16(2):865–873

    Google Scholar 

  23. Malfliet W, Hereman W (1996) The tanh method:exact solutions of nonlinear evolution and wave equations. Phys Scr 54(6):563–568

    Article  MathSciNet  Google Scholar 

  24. Moreira-Matias L, Gama J, Ferreira M, Mendes-Moreira J (2013) Predicting taxi–passenger demand using streaming data. IEEE Trans Intell Transp Syst 14(3):1393–1402

    Article  Google Scholar 

  25. Pan Z, Liang F, Wang C (2020) GMAN: A graph multi-attention network for traffic prediction, pp 1234–1241

  26. Park C, Lee C, Bahng H (2020) ST-GRAT: A novel spatio-temporal graph attention networks for accurately forecasting dynamically changing road speed. In: Proceedings of the 29th ACM International conference on information & knowledge management, p 1215–1224

  27. Polson NG, Sokolov VO (2017) Deep learning for short-term traffic flow prediction. Transportation Research Part C: Emerging Technologies 79:1–17

    Article  Google Scholar 

  28. Shih S, Sun F, Lee H (2019) Temporal pattern attention for multivariate time series forecasting. Mach Learn: 1421–1441

  29. Song C, Lin Y, Guo S (2020) Spatial-temporal synchronous graph convolutional networks: A new framework for spatial-temporal network data forecasting, pp 914–921

  30. Svetunkov I, Boylan JE (2020) State-space ARIMA for supply-chain forecasting. Int J Prod Res 58(3):818–827

    Article  Google Scholar 

  31. Wan H, Guo S, Yin K (2019) CTS-LSTM: LSTM-based neural networks for correlatedtime series prediction. Knowl.-Based Syst 191(8):1–10

    Google Scholar 

  32. Wang H, Yang Y, Liu B, Fujita H (2019) A study of graph-based system for multi-view clustering. Knowl-Based Syst 163:1009–1019

    Article  Google Scholar 

  33. Wang Y, Zheng Y, Xue Y (2014) Travel time estimation of a path using sparse trajectories. In: Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining, pp 25–347

  34. 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 

  35. Xiao Q, Dai J, Luo J, Fujita H (2019) Multi-view manifold regularized learning-based method for prioritizing candidate disease mirnas. Knowl-Based Syst: 118–129

  36. Xiong L, Wang C, Huang X, Zeng H (2019) An entropy regularization k-means algorithm with a new measure of between-cluster distance in subspace clustering. Entropy 21(683): 1–20

    MathSciNet  Google Scholar 

  37. Yao H, Wu F, Ke J (2018) Deep multi-view spatial-temporal network for taxi demand prediction. In: Proceedings of the 32th AAAI conference on artificial intelligence, pp 2588–2595

  38. Zhang J, Zheng Y, Qi D (2016) 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

  39. Zhang X, Huang C, Xu Y, Xia L, Dai P, Bo L, Zhang J, Zheng Y (2020) Traffic flow forecasting with spatial-temporal graph diffusion network. In: Proceedings of the 34th AAAI conference on artificial intelligence, pp 1–8

  40. Zhang X, Yang Y, Li T, Zhang Y, Fujita H (2020) CMC: a consensus multi-view clustering model for predicting alzheimer’s disease progression. Comput Methods Programs Biomed 199(105):895

    Google Scholar 

  41. Zhang Y, Yang Y, Li T, Fujita H (2019) A multitask multiview clustering algorithm in heterogeneous situations based on LLE and LE. Knowl-Based Syst 163(1):776–786

    Article  Google Scholar 

  42. Zhang Y, Yang Y, Zhou W, Ouyang X (2021) Multi-city traffic flow forecasting via multi-task learning. Appl Intell: 1–19

Download references

Acknowledgements

This research was funded by the National Natural Science Foundation of China under Grant No.62062033 and No.62067002, and the Natural Science Foundation of Jiangxi Province under Grant No.20192ACBL21006.

Author information

Authors and Affiliations

  1. School of Information Engineering Department, East China Jiaotong University, Nanchang, 330013, China

    Xiaohui Huang, Yuming Ye, Cheng Wang & Liyan Xiong

  2. Faculty of Science and Technology, University of Macau, E11, Macau, 999078, China

    Xiaofei Yang

Authors
  1. Xiaohui Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yuming Ye
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Cheng Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xiaofei Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Liyan Xiong
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yuming Ye.

Ethics declarations

Conflict of Interests

The authors declare that they have no conflict of interest.

Additional information

Publisher’s note

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

This article belongs to the Topical Collection: Special Issue on Multi-view Learning

Guest Editors: Guoqing Chao, Xingquan Zhu, Weiping Ding, Jinbo Bi and Shiliang Sun

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

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

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10489-021-02770-z

Keywords

Search

Navigation