Spatio-Temporal Pyramid Networks for Traffic Forecasting
Authors: 
Jia Hu, Chu Wang, Xianghong LinAuthors Info & Claims
Pages 339 - 354
Abstract
Traffic flow forecasting is an important part of smart city construction. Accurate traffic flow forecasting helps traffic management agencies to make timely adjustments, thus improving pedestrian travel efficiency and road utilization. However, this work is challenging due to the dynamic stochastic factors affecting the variation of traffic data and the spatially hidden behavior. Existing approaches generally use attention mechanism or graph neural networks to model correlation in temporal and spatial terms, and despite some progress in performance, they still ignore a number of practical situations: (1) Anomalous data due to traffic accidents or traffic congestion can affect the accuracy of modeling in the current moment and further create potential optimization problems for model training. (2) According to the directedness of the road, the hiding behavior between nodes should also be unidirectional and dynamic. In this paper, we propose a dynamic graph network with a pyramid structure, named PYNet, and use it for traffic flow forecasting tasks. Specifically, first we propose the Pyramid Constructor for transforming multivariate time series into a pyramid network with a multilevel structure, where the higher the level, the larger the range of time scales represented. Second, we perform Trend-Aware Attention top-down in the pyramid network, which gradually enables the lower-level time series to learn their long-term dependence in multiples, and effectively reduces the impact of outliers. Furthermore, to fully capture the hidden behavior in the spatial dimension, we learn an adaptive unidirectional graph and perform forward and backward diffusion convolution on the graph. Experimental results on two types of datasets show that PYNet outperforms the state-of-the-art baseline.
References
[1]
Bruna, J., Zaremba, W., Szlam, A., LeCun, Y.: Spectral networks and locally connected networks on graphs. arXiv preprint arXiv:1312.6203 (2013)
[2]
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)
[3]
Cho, K., et al.: Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078 (2014)
[4]
Connor JT, Martin RD, and Atlas LE Recurrent neural networks and robust time series prediction IEEE Trans. Neural Networks 1994 5 2 240-254
[5]
Guo S, Lin Y, Wan H, Li X, and Cong G Learning dynamics and heterogeneity of spatial-temporal graph data for traffic forecasting IEEE Trans. Knowl. Data Eng. 2021 34 11 5415-5428
[6]
Hochreiter S and Schmidhuber J Long short-term memory Neural Comput. 1997 9 8 1735-1780
[7]
Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016)
[8]
Kumar SV and Vanajakshi L Short-term traffic flow prediction using seasonal ARIMA model with limited input data Eur. Transp. Res. Rev. 2015 7 3 1-9
[9]
Lan, S., Ma, Y., Huang, W., Wang, W., Yang, H., Li, P.: DSTAGNN: dynamic spatial-temporal aware graph neural network for traffic flow forecasting. In: International Conference on Machine Learning, pp. 11906–11917. PMLR (2022)
[10]
Li F et al. Dynamic graph convolutional recurrent network for traffic prediction: benchmark and solution ACM Trans. Knowl. Discov. Data 2023 17 1 1-21
[11]
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)
[12]
Li, P., Fang, J., Chao, P., Zhao, P., Liu, A., Zhao, L.: JS-STDGN: a spatial-temporal dynamic graph network using JS-Graph for traffic prediction. In: Bhattacharya, A., et al. (eds.) Database Systems for Advanced Applications: 27th International Conference, DASFAA 2022, Virtual Event, 11–14 April 2022, Proceedings, Part I, pp. 191–206. Springer, Cham (2022).
[13]
Li, Q., Han, Z., Wu, X.M.: Deeper insights into graph convolutional networks for semi-supervised learning. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018)
[14]
Li, Y., Yu, R., Shahabi, C., Liu, Y.: Diffusion convolutional recurrent neural network: data-driven traffic forecasting. arXiv preprint arXiv:1707.01926 (2017)
[15]
Lu Z, Zhou C, Wu J, Jiang H, and Cui S Integrating granger causality and vector auto-regression for traffic prediction of large-scale WLANs KSII Trans. Internet Inf. Syst. 2016 10 1 136-151
[16]
Oreshkin, B.N., Amini, A., Coyle, L., Coates, M.: FC-GAGA: fully connected gated graph architecture for spatio-temporal traffic forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 9233–9241 (2021)
[17]
Park, C., et al.: 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, pp. 1215–1224 (2020)
[18]
Perozzi, B., Al-Rfou, R., Skiena, S.: DeepWalk: online learning of social representations. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 701–710 (2014)
[19]
Song, C., Lin, Y., Guo, S., Wan, H.: Spatial-temporal synchronous graph convolutional networks: a new framework for spatial-temporal network data forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 914–921 (2020)
[20]
Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. In: Advances in Neural Information Processing Systems, vol. 27 (2014)
[21]
Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, vol. 30 (2017)
[22]
Wu, Z., Pan, S., Long, G., Jiang, J., Chang, X., Zhang, C.: Connecting the dots: multivariate time series forecasting with graph neural networks. In: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 753–763 (2020)
[23]
Wu, Z., Pan, S., Long, G., Jiang, J., Zhang, C.: Graph WaveNet for deep spatial-temporal graph modeling. arXiv preprint arXiv:1906.00121 (2019)
[24]
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)
[25]
Yao, H., et al.: Deep multi-view spatial-temporal network for taxi demand prediction. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018)
[26]
Yu, B., Yin, H., Zhu, Z.: Spatio-temporal graph convolutional networks: a deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875 (2017)
[27]
Zhang, J., Zheng, Y., Qi, D.: Deep spatio-temporal residual networks for citywide crowd flows prediction. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017)
[28]
Zheng C et al. Spatio-temporal joint graph convolutional networks for traffic forecasting IEEE Trans. Knowl. Data Eng. 2023 17 691-703
[29]
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, vol. 34, pp. 1234–1241 (2020)
[30]
Zheng C, Fan X, Wen C, Chen L, Wang C, and Li J DeepSTD: mining spatio-temporal disturbances of multiple context factors for citywide traffic flow prediction IEEE Trans. Intell. Transp. Syst. 2019 21 9 3744-3755
[31]
Zhu, D., Cui, P., Wang, D., Zhu, W.: Deep variational network embedding in Wasserstein space. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2827–2836 (2018)
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Information
Published In
Sep 2023
801 pages
ISBN:978-3-031-43411-2
DOI:10.1007/978-3-031-43412-9
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.
Publisher
Springer-Verlag
Berlin, Heidelberg
Publication History
Published: 18 September 2023
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