Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
156 tokens/sec
GPT-4o
7 tokens/sec
Gemini 2.5 Pro Pro
45 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
38 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

A novel hybrid time-varying graph neural network for traffic flow forecasting (2401.10155v4)

Published 17 Jan 2024 in cs.LG

Abstract: Real-time and precise traffic flow prediction is vital for the efficiency of intelligent transportation systems. Traditional methods often employ graph neural networks (GNNs) with predefined graphs to describe spatial correlations among traffic nodes in urban road networks. However, these pre-defined graphs are limited by existing knowledge and graph generation methodologies, offering an incomplete picture of spatial correlations. While time-varying graphs based on data-driven learning have attempted to address these limitations, they still struggle with adequately capturing the inherent spatial correlations in traffic data. Moreover, most current methods for capturing dynamic temporal correlations rely on a unified calculation scheme using a temporal multi-head self-attention mechanism, which at some level might leads to inaccuracies. In order to overcome these challenges, we have proposed a novel hybrid time-varying graph neural network (HTVGNN) for traffic flow prediction. Firstly, a novel enhanced temporal perception multi-head self-attention mechanism based on time-varying mask enhancement was reported to more accurately model the dynamic temporal dependencies among distinct traffic nodes in the traffic network. Secondly, we have proposed a novel graph learning strategy to concurrently learn both static and dynamic spatial associations between different traffic nodes in road networks. Meanwhile, in order to enhance the learning ability of time-varying graphs, a coupled graph learning mechanism was designed to couple the graphs learned at each time step. Finally, the effectiveness of the proposed method HTVGNN was demonstrated with four real data sets. Simulation results revealed that HTVGNN achieves superior prediction accuracy compared to the state of the art spatio-temporal graph neural network models. Additionally, the ablation experiment verifies that the coupled graph learning mechanism can effectively improve the long-term prediction performance of HTVGNN.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (38)
  1. H. Yan, X. Ma, and Z. Pu, “Learning dynamic and hierarchical traffic spatiotemporal features with transformer,” IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 11, pp. 22 386–22 399, 2021.
  2. B. Pan, U. Demiryurek, and C. Shahabi, “Utilizing real-world transportation data for accurate traffic prediction,” in 2012 ieee 12th international conference on data mining.   IEEE, 2012, pp. 595–604.
  3. Y. Sun, G. Zhang, H. Yin et al., “Passenger flow prediction of subway transfer stations based on nonparametric regression model,” Discrete Dynamics in Nature and Society, vol. 2014, 2014.
  4. B. M. Williams, “Multivariate vehicular traffic flow prediction: evaluation of arimax modeling,” Transportation Research Record, vol. 1776, no. 1, pp. 194–200, 2001.
  5. B. M. Williams, P. K. Durvasula, and D. E. Brown, “Urban freeway traffic flow prediction: application of seasonal autoregressive integrated moving average and exponential smoothing models,” Transportation Research Record, vol. 1644, no. 1, pp. 132–141, 1998.
  6. E. Zivot and J. Wang, “Vector autoregressive models for multivariate time series,” Modeling financial time series with S-PLUS®, pp. 385–429, 2006.
  7. M. Awad, R. Khanna, M. Awad, and R. Khanna, “Support vector regression,” Efficient learning machines: Theories, concepts, and applications for engineers and system designers, pp. 67–80, 2015.
  8. J. Van Lint and C. Van Hinsbergen, “Short-term traffic and travel time prediction models,” Artificial Intelligence Applications to Critical Transportation Issues, vol. 22, no. 1, pp. 22–41, 2012.
  9. R. Fu, Z. Zhang, and L. Li, “Using lstm and gru neural network methods for traffic flow prediction,” in 2016 31st Youth academic annual conference of Chinese association of automation (YAC).   IEEE, 2016, pp. 324–328.
  10. Y.-S. Jeong, Y.-J. Byon, M. M. Castro-Neto, and S. M. Easa, “Supervised weighting-online learning algorithm for short-term traffic flow prediction,” IEEE Transactions on Intelligent Transportation Systems, vol. 14, no. 4, pp. 1700–1707, 2013.
  11. J. Bai, J. Zhu, Y. Song, L. Zhao, Z. Hou, R. Du, and H. Li, “A3t-gcn: Attention temporal graph convolutional network for traffic forecasting,” ISPRS International Journal of Geo-Information, vol. 10, no. 7, p. 485, 2021.
  12. K. Sun, Z. Zhu, and Z. Lin, “Adagcn: Adaboosting graph convolutional networks into deep models,” arXiv preprint arXiv:1908.05081, 2019.
  13. Z. Wu, S. Pan, F. Chen, G. Long, C. Zhang, and S. Y. Philip, “A comprehensive survey on graph neural networks,” IEEE transactions on neural networks and learning systems, vol. 32, no. 1, pp. 4–24, 2020.
  14. B. Yu, H. Yin, and Z. Zhu, “Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting,” arXiv preprint arXiv:1709.04875, 2017.
  15. M. Li and Z. Zhu, “Spatial-temporal fusion graph neural networks for traffic flow forecasting,” in Proceedings of the AAAI conference on artificial intelligence, vol. 35, no. 5, 2021, pp. 4189–4196.
  16. L. Bai, L. Yao, C. Li, X. Wang, and C. Wang, “Adaptive graph convolutional recurrent network for traffic forecasting,” Advances in neural information processing systems, vol. 33, pp. 17 804–17 815, 2020.
  17. P. Veličković, G. Cucurull, A. Casanova, A. Romero, P. Lio, and Y. Bengio, “Graph attention networks,” arXiv preprint arXiv:1710.10903, 2017.
  18. Y. Wang, X. Yu, S. Zhang, P. Zheng, J. Guo, L. Zhang, S. Hu, S. Cheng, and H. Wei, “Freeway traffic control in presence of capacity drop,” IEEE Transactions on Intelligent Transportation Systems, vol. 22, no. 3, pp. 1497–1516, 2020.
  19. I. Sutskever, O. Vinyals, and Q. V. Le, “Sequence to sequence learning with neural networks,” Advances in neural information processing systems, vol. 27, 2014.
  20. Y. Liu, H. Zheng, X. Feng, and Z. Chen, “Short-term traffic flow prediction with conv-lstm,” in 2017 9th International Conference on Wireless Communications and Signal Processing (WCSP).   IEEE, 2017, pp. 1–6.
  21. Z. Wu, S. Pan, G. Long, J. Jiang, and C. Zhang, “Graph wavenet for deep spatial-temporal graph modeling,” arXiv preprint arXiv:1906.00121, 2019.
  22. A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly et al., “An image is worth 16x16 words: Transformers for image recognition at scale,” arXiv preprint arXiv:2010.11929, 2020.
  23. J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, “Bert: Pre-training of deep bidirectional transformers for language understanding,” arXiv preprint arXiv:1810.04805, 2018.
  24. Z. Liu, Y. Lin, Y. Cao, H. Hu, Y. Wei, Z. Zhang, S. Lin, and B. Guo, “Swin transformer: Hierarchical vision transformer using shifted windows,” in Proceedings of the IEEE/CVF international conference on computer vision, 2021, pp. 10 012–10 022.
  25. X. Wang, Y. Ma, Y. Wang, W. Jin, X. Wang, J. Tang, C. Jia, and J. Yu, “Traffic flow prediction via spatial temporal graph neural network,” in Proceedings of the web conference 2020, 2020, pp. 1082–1092.
  26. B. Lu, X. Gan, H. Jin, L. Fu, and H. Zhang, “Spatiotemporal adaptive gated graph convolution network for urban traffic flow forecasting,” in Proceedings of the 29th ACM International conference on information & knowledge management, 2020, pp. 1025–1034.
  27. W. Chen, L. Chen, Y. Xie, W. Cao, Y. Gao, and X. Feng, “Multi-range attentive bicomponent graph convolutional network for traffic forecasting,” in Proceedings of the AAAI conference on artificial intelligence, vol. 34, no. 04, 2020, pp. 3529–3536.
  28. Y. Li, R. Yu, C. Shahabi, and Y. Liu, “Diffusion convolutional recurrent neural network: Data-driven traffic forecasting,” arXiv preprint arXiv:1707.01926, 2017.
  29. S. Guo, Y. Lin, N. Feng, C. Song, and H. Wan, “Attention based spatial-temporal graph convolutional networks for traffic flow forecasting,” in Proceedings of the AAAI conference on artificial intelligence, vol. 33, no. 01, 2019, pp. 922–929.
  30. C. Zheng, X. Fan, C. Wang, and J. Qi, “Gman: A graph multi-attention network for traffic prediction,” in Proceedings of the AAAI conference on artificial intelligence, vol. 34, no. 01, 2020, pp. 1234–1241.
  31. Y. Sun, X. Jiang, Y. Hu, F. Duan, K. Guo, B. Wang, J. Gao, and B. Yin, “Dual dynamic spatial-temporal graph convolution network for traffic prediction,” IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 12, pp. 23 680–23 693, 2022.
  32. J. Jiang, C. Han, W. X. Zhao, and J. Wang, “Pdformer: Propagation delay-aware dynamic long-range transformer for traffic flow prediction,” arXiv preprint arXiv:2301.07945, 2023.
  33. C. Chen, Y. Liu, L. Chen, and C. Zhang, “Bidirectional spatial-temporal adaptive transformer for urban traffic flow forecasting,” IEEE Transactions on Neural Networks and Learning Systems, 2022.
  34. C. Chen, K. Petty, A. Skabardonis, P. Varaiya, and Z. Jia, “Freeway performance measurement system: mining loop detector data,” Transportation Research Record, vol. 1748, no. 1, pp. 96–102, 2001.
  35. J. Liu, Y. Kang, H. Li, H. Wang, and X. Yang, “Stghtn: Spatial-temporal gated hybrid transformer network for traffic flow forecasting,” Applied Intelligence, vol. 53, no. 10, pp. 12 472–12 488, 2023.
  36. Q. Ma, W. Sun, J. Gao, P. Ma, and M. Shi, “Spatio-temporal adaptive graph convolutional networks for traffic flow forecasting,” IET Intelligent Transport Systems, vol. 17, no. 4, pp. 691–703, 2023.
  37. Y. Bao, J. Huang, Q. Shen, Y. Cao, W. Ding, Z. Shi, and Q. Shi, “Spatial–temporal complex graph convolution network for traffic flow prediction,” Engineering Applications of Artificial Intelligence, vol. 121, p. 106044, 2023.
  38. I. Loshchilov and F. Hutter, “Decoupled weight decay regularization,” arXiv preprint arXiv:1711.05101, 2017.
Citations (1)
View on Semantic Scholar

Summary

We haven't generated a summary for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Now
X Twitter Logo Streamline Icon: https://streamlinehq.com

Tweets