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STGMN: A gated multi-graph convolutional network framework for traffic flow prediction

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Abstract

Accurate traffic flow prediction is crucial for the development of intelligent transportation. It can not only effectively avoid traffic congestion and other traffic problems, but also provide a data basis for other complex tasks. The rapid development of social technology and the increasingly complex traffic environment lead to the emergence of massive traffic data. Traffic flow prediction as a spatial-temporal prediction problem has been widely concerned, but the traditional forecasting methods often ignore the spatial-temporal dependence, difficult to meet the prediction requirements. Therefore, this paper proposes a novel spatial-temporal model based on an attention one-dimension convolutional neural network (1D-CNN) and a gated interpretable framework, which models historical traffic data from the perspectives of time and space respectively. The core of the model proposed in this paper is to construct spatial-temporal blocks. First, a 1D-CNN based on channel attention mechanism and “inception” structure is proposed to extract temporal correlation. Then, considering the complexity of the actual traffic network, an interpretable multi-graph gated graph convolution framework is proposed to extract the spatial correlation. Finally, extensive experiments are carried out on real data sets, which prove the effectiveness of the proposed model, and it is very competitive compared with some state-of-the-art methods.

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References

  1. Lana I, Del Ser J, Velez M, Vlahogianni EI (2018) Road traffic forecasting: Recent advances and new challenges. IEEE Intelligent Transportation Systems Magazine 10(2):93–109

    Article  Google Scholar 

  2. Ye J, Zhao J, Ye K, Xu C (2020) How to build a graph-based deep learning architecture in traffic domain: A survey. IEEE Trans Intell Transp Syst :1–21

  3. 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

    Article  Google Scholar 

  4. Lee K, Eo M, Jung E, Yoon Y, Rhee W (2021) Short-term traffic prediction with deep neural networks: A survey. IEEE Access 9:54739–54756

    Article  Google Scholar 

  5. Luo X, Niu L, Zhang S (2018) An algorithm for traffic flow prediction based on improved sarima and ga. KSCE Journal of Civil Engineering 22(10):4107–4115

    Article  Google Scholar 

  6. Kumar SV, Vanajakshi L (2015) Short-term traffic flow prediction using seasonal arima model with limited input data. European Transport Research Review 7(3):1–9

    Article  Google Scholar 

  7. Cong Y, Wang J, Li X (2016) Traffic flow forecasting by a least squares support vector machine with a fruit fly optimization algorithm. Procedia Engineering 137:59–68

    Article  Google Scholar 

  8. Makridakis S, Spiliotis E, Assimakopoulos V (2018) Statistical and machine learning forecasting methods: Concerns and ways forward. PloS ONE 13(3):e0194889

    Article  Google Scholar 

  9. Li H, Wang Y, Xu X, Qin L, Zhang H (2019) Short-term passenger flow prediction under passenger flow control using a dynamic radial basis function network. Applied Soft Computing 83:105620

    Article  Google Scholar 

  10. Lv Z, Xu J, Zheng K, Yin H, Zhao P, Zhou X (2018) Lc-rnn: A deep learning model for traffic speed prediction. Int Joint Conf Artif Intell :3470–3476

  11. Yin X, Wu G, Wei J, Shen Y, Qi H, Yin B (2021) Multi-stage attention spatial-temporal graph networks for traffic prediction. Neurocomputing 428:42–53

    Article  Google Scholar 

  12. Guo S, Lin Y, Li S, Chen Z, Wan H (2019) Deep spatial-temporal 3d convolutional neural networks for traffic data forecasting. IEEE Transactions on Intelligent Transportation Systems 20(10):3913–3926

    Article  Google Scholar 

  13. de Medrano R, Aznarte JL (2020) A spatio-temporal attention-based spot-forecasting framework for urban traffic prediction. Applied Soft Computing 96:106615

    Article  Google Scholar 

  14. Peng H, Du B, Liu M, Liu M, Ji S, Wang S, Zhang X, He L (2021) Dynamic graph convolutional network for long-term traffic flow prediction with reinforcement learning. Information Sciences 578:401–416

    Article  MathSciNet  Google Scholar 

  15. Xiao Y, Yin H, Zhang Y, Qi H, Zhang Y, Liu Z (2021) A dual-stage attention-based conv-lstm network for spatio-temporal correlation and multivariate time series prediction. International Journal of Intelligent Systems 36(5):2036–2057

    Article  Google Scholar 

  16. Yu H, Wu Z, Wang S, Wang Y, Ma X (2017) Spatiotemporal recurrent convolutional networks for traffic prediction in transportation networks. Sensors 17(7):1501

    Article  Google Scholar 

  17. Wu Z, Pan S, Chen F, Long G, Zhang C, Philip SY (2020) A comprehensive survey on graph neural networks. IEEE Transactions on Neural Networks and Learning Systems 32(1):4–24

    Article  MathSciNet  Google Scholar 

  18. Zhang Z, Cui P, Zhu W (2020) Deep learning on graphs: A survey. IEEE Transactions on Knowledge and Data Engineering 34(1):249–270

    Article  Google Scholar 

  19. Liang Y, Ke S, Zhang J, Yi X, Zheng Y (2018) Geoman: Multi-level attention networks for geo-sensory time series prediction. Int Joint Conf Artif Intell :3428–3434

  20. Zheng C, Fan X, Wang C, Qi J (2020) Gman: A graph multi-attention network for traffic prediction. Proceedings of the AAAI Conference on Artificial Intelligence 34(01):1234–1241

    Article  Google Scholar 

  21. Yao H, Tang X, Wei H, Zheng G, Li Z (2019) Revisiting spatial-temporal similarity: A deep learning framework for traffic prediction. Proc AAAI Conf Artif Intell 33(01):5668–5675

    Google Scholar 

  22. Zhao L, Song Y, Zhang C, Liu Y, Wang P, Lin T, Deng M, Li H (2019) T-gcn: A temporal graph convolutional network for traffic prediction. IEEE Transactions on Intelligent Transportation Systems 21(9):3848–3858

    Article  Google Scholar 

  23. Yu B, Yin H, Zhu Z (2018) Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. Int Joint Conf Artif Intell :3634–3640

  24. Guo S, Lin Y, Feng N, Song C, Wan H (2019) Attention based spatial-temporal graph convolutional networks for traffic flow forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 33(01):922–929

    Article  Google Scholar 

  25. Li Y, Yu R, Shahabi C, Liu Y (2018) Diffusion convolutional recurrent neural network: Data-driven traffic forecasting. Int Conf Learn Representations :1–16

  26. Fang S, Zhang Q, Meng G, Xiang S, Pan C (2019) Gstnet: Global spatial-temporal network for traffic flow prediction. Int Joint Conf Artif Intell :2286–2293

  27. Song C, Lin Y, Guo S, Wan H (2020) Spatial-temporal synchronous graph convolutional networks: A new framework for spatial-temporal network data forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 34(01):914–921

    Article  Google Scholar 

  28. Tedjopurnomo DA, Bao Z, Zheng B, Choudhury F, Qin A (2020) A survey on modern deep neural network for traffic prediction: Trends, methods and challenges. IEEE Transactions on Knowledge & Data Engineering 01:1

    Article  Google Scholar 

  29. Zhang W, Yu Y, Qi Y, Shu F, Wang Y (2019) Short-term traffic flow prediction based on spatio-temporal analysis and cnn deep learning. Transportmetrica A: Transport Science 15(2):1688–1711

    Article  Google Scholar 

  30. Bogaerts T, Masegosa AD, Angarita-Zapata JS, Onieva E, Hellinckx P (2020) A graph cnn-lstm neural network for short and long-term traffic forecasting based on trajectory data. Transportation Research Part C: Emerging Technologies 112:62–77

    Article  Google Scholar 

  31. Zhang Q, Chang J, Meng G, Xiang S, Pan C (2020) Spatio-temporal graph structure learning for traffic forecasting. Proceedings of the AAAI Conference on Artificial Intelligence 34(01):1177–1185

    Article  Google Scholar 

  32. Kipf TN, Welling M Semi-supervised classification with graph convolutional networks. International Conference on Learning Representations

  33. Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A (2015) Going deeper with convolutions. Proceedings of the IEEE conference on computer vision and pattern recognition. pp 1–9

  34. Wang Q, Wu B, Zhu P, Li P, Zuo W, Hu Q (2020) Eca-net: Efficient channel attention for deep convolutional neural networks. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp 11531–11539

  35. Oreshkin BN, Carpov D, Chapados N, Bengio Y N-beats: Neural basis expansion analysis for interpretable time series forecasting. International Conference on Learning Representations

  36. Wu Z, Pan S, Long G, Jiang J, Zhang C (2019) Graph wavenet for deep spatial-temporal graph modeling, International Joint Conference on Artificial Intelligence. pp 1907–1913

  37. Chen C, Petty K, Skabardonis A, Varaiya P, Jia Z (2001) Freeway performance measurement system: mining loop detector data. Transportation Research Record 1748(1):96–102

    Article  Google Scholar 

  38. Cortes C, Vapnik V (1995) Support-vector networks. Machine learning 20(3):273–297

    Article  Google Scholar 

  39. Chung J, Gulcehre C, Cho K, Bengio Y (2014) Empirical evaluation of gated recurrent neural networks on sequence modeling. NIPS 2014 workshop on deep learning

  40. Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Computation 9(8):1735–1780

    Article  Google Scholar 

  41. Bai L, Yao L, Li C, Wang X, Wang C (2020) Adaptive graph convolutional recurrent network for traffic forecasting. 34th conference on neural information processing systems, vol 33. pp 17804–17815

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Acknowledgements

This paper is supported by National Key R&D Program of China (2018YFB1004300).

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Authors and Affiliations

  1. School of Computer Science and Engineering, Southeast University, Nanjing, 211189, China

    Qingjian Ni & Meng Zhang

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  1. Qingjian Ni
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  2. Meng Zhang
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Correspondence to Qingjian Ni.

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Ni, Q., Zhang, M. STGMN: A gated multi-graph convolutional network framework for traffic flow prediction. Appl Intell 52, 15026–15039 (2022). https://doi.org/10.1007/s10489-022-03224-w

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