Skip to main content
SpringerLink
Log in

Deep spatial-temporal networks for crowd flows prediction by dilated convolutions and region-shifting attention mechanism

  • Published:
Applied Intelligence Aims and scope Submit manuscript

Abstract

Flow prediction at a citywide level is of great significance to traffic management and public safety. Since deep learning has achieved success to deal with complex nonlinear problems, it has drawn increasing attention on making crowd flows prediction through neural networks. Generally, convolutional neural network (CNN) and recurrent neural network (RNN) have been applied to model the spatial-temporal dependency of the city. However, there are still two major challenges in predicting flows. First, it is difficult to train the model with the ability to capture both the nearby and distant spatial dependency by deep local convolutions. Second, daily and weekly patterns in temporal dependency are not strictly periodic for their dynamic temporal shifting in each region. To address these issues, we propose a novel deep learning model which called Local-Dilated Region-Shifting Network (LDRSN). LDRSN combines local convolutions with dilated convolutions to learn the nearby and distant spatial dependency. Furthermore, a new region-level attention mechanism is proposed to model the temporal shifting which varies by region. In the experiments, we compare the proposed method with other state-of-the-art methods in two real-world crowd flows datasets. The Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE) were used as the evaluation indexes. The experiment results show the effectiveness of the proposed model.

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.

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

Similar content being viewed by others

References

  1. Tang K, Chen S, Khattak AJ (2018) A spatial–temporal multitask collaborative learning model for multistep traffic flow prediction[J]. Transp Res Rec 2672(45):1–13

    Article  Google Scholar 

  2. Zhang H, Wang X, Cao J, et al (2018) A multivariate short-term traffic flow forecasting method based on wavelet analysis and seasonal time series[J]. Appl Intell 48:3827–3838

    Article  Google Scholar 

  3. Rota BCR, Simic M (2016) Traffic flow optimization on freeways[J]. Procedia Comput Sci 96:1637–1646

    Article  Google Scholar 

  4. Wang Y, Geroliminis N, Leclercq L (2016) Recent advances in ITS, traffic flow theory, and network operations[J]. Transportation Research Part C: Emerging Technologies 100(68):507–508

    Article  Google Scholar 

  5. Min X, Hu J, Zhang Z (2010) Urban traffic network modeling and short-term traffic flow forecasting based on GSTARIMA model[C]. In: 13th international IEEE conference on intelligent transportation systems. IEEE, pp 1535–1540

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

    Article  Google Scholar 

  7. Zhang X, Onieva E, Perallos A, et al (2014) Hierarchical fuzzy rule-based system optimized with genetic algorithms for short term traffic congestion prediction[J]. Transportation Research, 43C(pt.2):127–142

  8. Chen FC, Jahanshahi MR (2018) NB-CNN deep learning-based crack detection using convolutional neural network and naïve Bayes data fusion[J]. IEEE Trans Ind Electron 65(5):4392– 4400

    Article  Google Scholar 

  9. Xu W, Lebeau JM (2018) A deep convolutional neural network to analyze position averaged convergent beam electron diffraction patterns[J]. Ultramicroscopy 188:59–69

    Article  Google Scholar 

  10. Zhang J, Zheng Y, Qi D, et al (2016) DNN-Based prediction model for spatio-temporal data[C]. In: Proceedings of the 24th ACM SIGSPATIAL international conference on advances in geographic information systems. ACM, p 92

  11. Yu R, Li Y, Shahabi C, et al (2017) Deep learning: a generic approach for extreme condition traffic forecasting[C]. In: Proceedings of the 2017 SIAM international conference on data mining. Society for industrial and applied mathematics, pp 777–785

  12. Cheng X, Zhang R, Zhou J, et al (2018) Deeptransport: learning spatial-temporal dependency for traffic condition forecasting[C]. In: 2018 international joint conference on neural networks (IJCNN). IEEE, pp 1–8

  13. Xingjian SHI, Chen Z, Wang H, et al (2015) Convolutional LSTM network: a machine learning approach for precipitation nowcasting[C]. In: Advances in neural information processing systems, pp 802–810

  14. Ballas N, Yao L, Pal C, et al (2015) Delving deeper into convolutional networks for learning video representations[J]. arXiv:1511.06432

  15. Zonoozi A, Kim J, Li XL, et al (2018) Periodic-CRN: a convolutional recurrent model for crowd density prediction with recurring periodic patterns[C]. IJCAI, pp 3732–3738

  16. Zhou X, Shen Y, Zhu Y, et al (2018) Predicting multi-step citywide passenger demands using attention-based neural networks[C]. In: Proceedings of the eleventh ACM international conference on web search and data mining. ACM, pp 736–744

  17. Zhang J, Zheng Y, Qi D (2017) Deep spatio-temporal residual networks for citywide crowd flows prediction[C]. In: Thirty-first AAAI conference on artificial intelligence

  18. Yao H, Tang X, Wei H, et al (2019) Revisiting spatial-temporal similarity: a deep learning framework for traffic prediction[C]. In: 2019 AAAI conference on artificial intelligence (AAAI’19)

  19. Zhou H, Hirasawa K (2019) Spatiotemporal traffic network analysis: technology and applications[J]. Knowl Inf Syst 60(1):25–61

    Article  Google Scholar 

  20. Min X, Hu J, Zhang Z (2010) Urban traffic network modeling and short-term traffic flow forecasting based on GSTARIMA model[C]. In: 13th international IEEE conference on intelligent transportation systems. IEEE, pp 1535–1540

  21. Kumar SV, Vanajakshi L (2015) Short-term traffic flow prediction using seasonal ARIMA model with limited input data[J]. European Transport Res Rev 7(3):21

    Article  Google Scholar 

  22. Kuang P, Ma T, Chen Z, et al (2019) Image super-resolution with densely connected convolutional networks[J]. Appl Intell 49(1):125–136

    Article  Google Scholar 

  23. Zhang J, Zheng Y, Qi D, et al (2016) DNN-Based prediction model for spatio-temporal data[C]. In: Proceedings of the 24th ACM SIGSPATIAL international conference on advances in geographic information systems. ACM, p 92

  24. He K, Zhang X, Ren S, et al (2016) Deep residual learning for image recognition[C]. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770–778

  25. Zhang J, Zheng Y, Qi D, et al (2018) Predicting citywide crowd flows using deep spatio-temporal residual networks[J]. Artif Intell 259:147–166

    Article  MathSciNet  Google Scholar 

  26. Xingjian SHI, Chen Z, Wang H, et al (2015) Convolutional LSTM network: a machine learning approach for precipitation nowcasting[C]. In: Advances in neural information processing systems, pp 802–810

  27. Zonoozi A, Kim J, Li XL, et al (2018) Periodic-CRN: a convolutional recurrent model for crowd density prediction with recurring periodic patterns[C]. IJCAI, pp 3732–3738

  28. Yao H, Wu F, Ke J, et al (2018) Deep multi-view spatial-temporal network for taxi demand prediction[C]. In: Thirty-second AAAI conference on artificial intelligence, pp 2588–2595

  29. Wang Y, Wang G, Chen C, et al (2019) Multi-scale dilated convolution of convolutional neural network for image denoising[J]. Multimedia Tools and Applications 78(14):19945–19960

    Article  Google Scholar 

  30. Wu Y, et al (2018) A hybrid deep learning based traffic flow prediction method and its understanding[J]. Transportation Research Part C Emerging Technologies 90:166–180

    Article  Google Scholar 

  31. Gao P, Zhang Q, Wang F, et al (2019) Learning reinforced attentional representation for end-to-end visual tracking[J]. Information Sciences

  32. Gao P, Yuan R, Wang F, et al (2020) Siamese attentional keypoint network for high performance visual tracking. Knowl-Based Syst 193:105448

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by the Major International Science and Technology Innovation Cooperation Project of the Ministry of Science and Technology, 2017YFE0120600.

Author information

Authors and Affiliations

  1. Stat Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, China

    Chujie Tian, Zheng Hu & Jian Ma

  2. Key Laboratory of Universal Wireless Communications, Ministry of Education, Beijing University of Posts and Telecommunications, Beijing, China

    Xinning Zhu

Authors
  1. Chujie Tian
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xinning Zhu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zheng Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jian Ma
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Zheng Hu.

Additional information

Publisher’s note

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Tian, C., Zhu, X., Hu, Z. et al. Deep spatial-temporal networks for crowd flows prediction by dilated convolutions and region-shifting attention mechanism. Appl Intell 50, 3057–3070 (2020). https://doi.org/10.1007/s10489-020-01698-0

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10489-020-01698-0

Keywords

Search

Navigation