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Multi-scale fusion dynamic graph convolutional recurrent network for traffic forecasting

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Abstract

Traffic forecasting plays an essential role in urban planning and traffic management. Nevertheless, the intricate spatio-temporal connections in traffic data make traffic prediction particularly tough. Although many traffic prediction methods have been developed, most of them fail to effectively model the decomposition of multiple traffic modes in traffic conditions, and it is also difficult to dynamically capture spatio-temporal dependencies between roads without relying on prior knowledge. A framework called Multi-scale Fusion Dynamic Graph Convolutional Recurrent Network (MDGCRN) is developed to tackle these difficulties. The approach initially separates intricate traffic conditions into trend and detail components, which are subsequently predicted independently using the dynamic graph convolutional recurrent network. Within this network, we propose a technique for creating dynamic graphs that, when combined with GRU, effectively captures short-term and long-term temporal information as well as spatial information. Comprehensive experiments conducted on three large-scale datasets demonstrate that MDGCRN outperforms existing state-of-the-art baselines across all metrics.

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Data availibility

All data sets analyzed during this study to support the results of the article are publicly available.

References

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

    Article  MATH  Google Scholar 

  2. Cirstea, R.-G., Kieu, T., Guo, C., Yang, B., Pan, S.J.: Enhancenet: Plugin neural networks for enhancing correlated time series forecasting. In: 2021 IEEE 37th International Conference on Data Engineering (ICDE), pp. 1739–1750 (2021). IEEE

  3. Pan, Z., Zhang, W., Liang, Y., Zhang, W., Yu, Y., Zhang, J., Zheng, Y.: Spatio-temporal meta learning for urban traffic prediction. IEEE Transac Knowled Data Eng 34(3), 1462–1476 (2020)

    Article  MATH  Google Scholar 

  4. Miao, H., Zhao, Y., Guo, C., Yang, B., Zheng, K., Huang, F., Xie, J., Jensen, C.S.: A unified replay-based continuous learning framework for spatio-temporal prediction on streaming data. arXiv preprint arXiv:2404.14999 (2024)

  5. Xu, Y., Cai, X., Wang, E., Liu, W., Yang, Y., Yang, F.: Dynamic traffic correlations based spatio-temporal graph convolutional network for urban traffic prediction. Inf Sci 621, 580–595 (2023)

    Article  MATH  Google Scholar 

  6. Jiang, R., Wang, Z., Yong, J., Jeph, P., Chen, Q., Kobayashi, Y., Song, X., Fukushima, S., Suzumura, T.: Spatio-temporal meta-graph learning for traffic forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 37, pp. 8078–8086 (2023)

  7. Wang, Y.-W., Dai, Z.-X., Wang, G.-S., Chen, L., Xia, Y.-Z., Zhou, Y.-H.: A hybrid physics-informed data-driven neural network for co2 storage in depleted shale reservoirs. Petrol Sci 21(1), 286–301 (2024)

    Article  MATH  Google Scholar 

  8. Chakraborty, S., Calo, S.B., Wen, J.: Using disentangled learning to train an interpretable deep learning model. Google Patents. US Patent App. 17/133,437 (2022)

  9. Wen, J., Yang, S., Wang, C.D., Jiang, Y., Li, R.: Feature-splitting algorithms for ultrahigh dimensional quantile regression. Journal of Econometrics, 105426 (2023)

  10. Geng, X., Li, Y., Wang, L., Zhang, L., Yang, Q., Ye, J., Liu, Y.: Spatiotemporal multi-graph convolution network for ride-hailing demand forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 3656–3663 (2019)

  11. Lu, P., Wang, Y.(2024) : Rdgan: prediction of circrna-disease associations via resistance distance and graph attention network. IEEE/ACM Transactions on Computational Biology and Bioinformatics

  12. Liu, C., Yang, S., Xu, Q., Li, Z., Long, C., Li, Z., Zhao, R.: Spatial-temporal large language model for traffic prediction. arXiv preprint arXiv:2401.10134 (2024)

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

  14. Yao, H., Tang, X., Wei, H., Zheng, G., Li, Z.: Revisiting spatial-temporal similarity: A deep learning framework for traffic prediction. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 5668–5675 (2019)

  15. Li, Y., Yu, R., Shahabi, C., Liu, Y.: Diffusion convolutional recurrent neural network: Data-driven traffic forecasting. arXiv preprint arXiv:1707.01926 (2017)

  16. Li, F., Feng, J., Yan, H., Jin, G., Yang, F., Sun, F., Jin, D., Li, Y.: Dynamic graph convolutional recurrent network for traffic prediction: Benchmark and solution. ACM Transac Knowl Discov Data 17(1), 1–21 (2023)

    MATH  Google Scholar 

  17. Weng, W., Fan, J., Wu, H., Hu, Y., Tian, H., Zhu, F., Wu, J.: A decomposition dynamic graph convolutional recurrent network for traffic forecasting. Pattern Recognit 142, 109670 (2023)

    Article  MATH  Google Scholar 

  18. Wang, X., Ma, Y., Wang, Y., Jin, W., Wang, X., Tang, J., Jia, C., Yu, J.: Traffic flow prediction via spatial temporal graph neural network. In: Proceedings of the Web Conference 2020, pp. 1082–1092 (2020)

  19. Wang, S., Zhang, M., Miao, H., Peng, Z., Yu, P.S.: Multivariate correlation-aware spatio-temporal graph convolutional networks for multi-scale traffic prediction. ACM Transac Intell Sys Technol 13(3), 1–22 (2022)

    Article  MATH  Google Scholar 

  20. Choi, J., Choi, H., Hwang, J., Park, N.: Graph neural controlled differential equations for traffic forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 36, pp. 6367–6374 (2022)

  21. Zhang, D., Li, J.: Multi-view fusion neural network for traffic demand prediction. Inf. Sci. 646, 119303 (2023)

    Article  MATH  Google Scholar 

  22. Zhang, Z., Zhao, X., Miao, H., Zhang, C., Zhao, H., Zhang, J.: Autostl: Automated spatio-temporal multi-task learning. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 37, pp. 4902–4910 (2023)

  23. Wu, X., Zhang, D., Guo, C., He, C., Yang, B., Jensen, C.S.: Autocts: Automated correlated time series forecasting. Proceed. VLDB Endowm. 15(4), 971–983 (2021)

    Article  MATH  Google Scholar 

  24. Zhao, L., Song, Y., Zhang, C., Liu, Y., Wang, P., Lin, T., Deng, M., Li, H.: T-gcn: A temporal graph convolutional network for traffic prediction. IEEE Transac. Intell. Transport. Sys. 21(9), 3848–3858 (2019)

    Article  MATH  Google Scholar 

  25. Wu, Z., Pan, S., Long, G., Jiang, J., Zhang, C.: Graph wavenet for deep spatial-temporal graph modeling. arXiv preprint arXiv:1906.00121 (2019)

  26. Bai, L., Yao, L., Li, C., Wang, X., Wang, C.: Adaptive graph convolutional recurrent network for traffic forecasting. Adv. Neural Inf. Process. Sys. 33, 17804–17815 (2020)

    MATH  Google Scholar 

  27. Hamilton, J.D.: Time Series Analysis. Princeton university press, ??? (2020)

  28. Lütkepohl, H.: New Introduction to Multiple Time Series Analysis. Springer, ??? (2005)

  29. Wu, C.-H., Ho, J.-M., Lee, D.-T.: Travel-time prediction with support vector regression. IEEE Transac. Intell. Transport. Sys. 5(4), 276–281 (2004)

    Article  MATH  Google Scholar 

  30. Williams, B.M., Hoel, L.A.: Modeling and forecasting vehicular traffic flow as a seasonal arima process: Theoretical basis and empirical results. J. Transport. Eng. 129(6), 664–672 (2003)

    Article  MATH  Google Scholar 

  31. Cai, L., Janowicz, K., Mai, G., Yan, B., Zhu, R.: Traffic transformer: Capturing the continuity and periodicity of time series for traffic forecasting. Transac. GIS 24(3), 736–755 (2020)

    Article  Google Scholar 

  32. Yu, F., Koltun, V.: Multi-scale context aggregation by dilated convolutions. arXiv preprint arXiv:1511.07122 (2015)

  33. Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Advances in neural information processing systems 27 (2014)

  34. Wang, S., Zhang, M., Miao, H., Yu, P.S.: Mt-stnets: Multi-task spatial-temporal networks for multi-scale traffic prediction. In: Proceedings of the 2021 SIAM International Conference on Data Mining (SDM), pp. 504–512 (2021). SIAM

  35. Shao, Z., Zhang, Z., Wei, W., Wang, F., Xu, Y., Cao, X., Jensen, C.S.: Decoupled dynamic spatial-temporal graph neural network for traffic forecasting. arXiv preprint arXiv:2206.09112 (2022)

  36. Liu, C., Xu, Q., Miao, H., Yang, S., Zhang, L., Long, C., Li, Z., Zhao, R.: Timecma: Towards llm-empowered time series forecasting via cross-modality alignment. arXiv preprint arXiv:2406.01638 (2024)

  37. Lai, Z., Li, H., Zhang, D., Zhao, Y., Qian, W., Jensen, C.S.: E2usd: Efficient-yet-effective unsupervised state detection for multivariate time series. In: Proceedings of the ACM on Web Conference 2024, pp. 3010–3021 (2024)

  38. Lai, Z., Zhang, D., Li, H., Jensen, C.S., Lu, H., Zhao, Y.: Lightcts \(^{\ast }\): Lightweight correlated time series forecasting enhanced with model distillation. IEEE Transactions on Knowledge and Data Engineering (2024)

  39. Daubechies, I.: Ten lectures on wavelets. Society for industrial and applied mathematics (1992)

  40. Zang, Y., Ni, F., Feng, Z., Cui, S., Ding, Z.: Wavelet transform processing for cellular traffic prediction in machine learning networks. In: 2015 IEEE China Summit and International Conference on Signal and Information Processing (ChinaSIP), pp. 458–462 (2015). IEEE

  41. Lau, K.-M., Weng, H.: Climate signal detection using wavelet transform: How to make a time series sing. Bull. Amr. Meteorol. Soc. 76(12), 2391–2402 (1995)

    Article  MATH  Google Scholar 

  42. Martínez, B., Gilabert, M.A.: Vegetation dynamics from ndvi time series analysis using the wavelet transform. Remote Sens. Environ. 113(9), 1823–1842 (2009)

    Article  MATH  Google Scholar 

  43. Cochran, W.T., Cooley, J.W., Favin, D.L., Helms, H.D., Kaenel, R.A., Lang, W.W., Maling, G.C., Nelson, D.E., Rader, C.M., Welch, P.D.: What is the fast fourier transform? Proceed. IEEE 55(10), 1664–1674 (1967)

    Article  Google Scholar 

  44. Flandrin, P., Rilling, G., Goncalves, P.: Empirical mode decomposition as a filter bank. IEEE Signal Process. Lett. 11(2), 112–114 (2004)

    Article  MATH  Google Scholar 

  45. Dunne, S., Ghosh, B.: Weather adaptive traffic prediction using neurowavelet models. IEEE Transac. Intell. Transport. Sys. 14(1), 370–379 (2013)

    Article  MATH  Google Scholar 

  46. Bekhtin, Y.S., Balanev, K.S.: Simulation modeling network traffic behavior using regression analysis in wavelet domain. In: 2024 6th International Youth Conference on Radio Electronics, Electrical and Power Engineering (REEPE), pp. 1–6 (2024). IEEE

  47. Liao, K., Zhou, W.: An ewt-ensemlstm-lssa model for metro passengers volume prediction. IEEE Access (2023)

  48. Madan, R., Mangipudi, P.S.: Predicting computer network traffic: a time series forecasting approach using dwt, arima and rnn. In: 2018 Eleventh International Conference on Contemporary Computing (IC3), pp. 1–5 (2018). IEEE

  49. Bengio, Y., Courville, A., Vincent, P.: Representation learning: A review and new perspectives. IEEE Transac. Pattern Anal. Machine Intell. 35(8), 1798–1828 (2013)

    Article  MATH  Google Scholar 

  50. Cho, K., Van Merriënboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning phrase representations using rnn encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078 (2014)

  51. Chen, W., Chen, L., Xie, Y., Cao, W., Gao, Y., Feng, X.: Multi-range attentive bicomponent graph convolutional network for traffic forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 3529–3536 (2020)

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

  53. Chen, Y., Segovia, I., Gel, Y.R.: Z-gcnets: Time zigzags at graph convolutional networks for time series forecasting. In: International Conference on Machine Learning, pp. 1684–1694 (2021). PMLR

  54. Jiang, J., Han, C., Zhao, W.X., Wang, J.: Pdformer: Propagation delay-aware dynamic long-range transformer for traffic flow prediction. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 37, pp. 4365–4373 (2023)

  55. Fang, Y., Qin, Y., Luo, H., Zhao, F., Zheng, K.: Stwave+: A multi-scale efficient spectral graph attention network with long-term trends for disentangled traffic flow forecasting. IEEE Transactions on Knowledge and Data Engineering (2023)

  56. Qiu, X., Hu, J., Zhou, L., Wu, X., Du, J., Zhang, B., Guo, C., Zhou, A., Jensen, C.S., Sheng, Z., et al.: Tfb: Towards comprehensive and fair benchmarking of time series forecasting methods. arXiv preprint arXiv:2403.20150 (2024)

  57. Cho, K., Van Merriënboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., Bengio, Y.: Learning phrase representations using rnn encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078 (2014)

  58. Yu, B., Yin, H., Zhu, Z.: Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875 (2017)

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

  60. Huang, S., Wang, D., Wu, X., Tang, A.: Dsanet: Dual self-attention network for multivariate time series forecasting. In: Proceedings of the 28th ACM International Conference on Information and Knowledge Management, pp. 2129–2132 (2019)

  61. Li, M., Zhu, Z.: Spatial-temporal fusion graph neural networks for traffic flow forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 4189–4196 (2021)

  62. Fang, Z., Long, Q., Song, G., Xie, K.: Spatial-temporal graph ode networks for traffic flow forecasting. In: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, pp. 364–373 (2021)

  63. Fahd, S.: Recent advances in image super-resolution: Exploring diffusion models, wavelet-based approaches, and federated learning techniques for high-fidelity image enhancement. Int. J. Machine Intell. Smart Appl. 14(9), 1–15 (2024)

    MATH  Google Scholar 

  64. Hassan, O., Al-Rawi, M.: Advancements in image super-resolution: Diffusion models, wavelets, and federated learning. Appl. Res Artif. Intell. Cloud Comput. 7(6), 223–233 (2024)

    MATH  Google Scholar 

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Funding

This work is supported by the Fund of the Natural Science Foundation of Shandong Province (No. ZR2020MF005, No. ZR2020MF006, No. ZR2022LZH015), China University of Petroleum (East China) (No. 27RA2307007), Shandong Province Youth Innovation and Technology Program Innovation Team (No. 2023KJ070).

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Authors and Affiliations

  1. Qingdao Institute of Software, College of Computer Science and Technology, China University of Petroleum (East China), Changjiang Road, Qingdao, 266580, China

    Junbi Xiao, Wenjing Zhang, Yuhao Zhou & Yunhuan Cong

  2. College of Computer Science and Technology, Zhejiang Universityof Technology, Zhejiang, 310023, China

    Wenchao Weng

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  1. Junbi Xiao
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  2. Wenjing Zhang
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Correspondence to Yuhao Zhou.

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Xiao, J., Zhang, W., Weng, W. et al. Multi-scale fusion dynamic graph convolutional recurrent network for traffic forecasting. Cluster Comput 28, 150 (2025). https://doi.org/10.1007/s10586-024-04869-7

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