STGSA: A Novel Spatial-Temporal Graph Synchronous Aggregation Model for Traffic Prediction

Zebing Wei; Hongxia Zhao; Zhishuai Li; Xiaojie Bu; Yuanyuan Chen; Xiqiao Zhang; Yisheng Lv; Fei-Yue Wang

doi:10.1109/JAS.2023.123033

Volume 10 Issue 1

Jan. 2023

IEEE/CAA Journal of Automatica Sinica

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Article Navigation > IEEE/CAA Journal of Automatica Sinica > 2023 > 10(1): 226-238

Z. B. Wei, H. X. Zhao, Z. S. Li, X. J. Bu, Y. Y. Chen, X. Q. Zhang, Y. S. Lv, and F.-Y. Wang, “STGSA: A novel spatial-temporal graph synchronous aggregation model for traffic prediction,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 1, pp. 226–238, Jan. 2023. doi: 10.1109/JAS.2023.123033

Citation:

Z. B. Wei, H. X. Zhao, Z. S. Li, X. J. Bu, Y. Y. Chen, X. Q. Zhang, Y. S. Lv, and F.-Y. Wang, “STGSA: A novel spatial-temporal graph synchronous aggregation model for traffic prediction,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 1, pp. 226–238, Jan. 2023. doi: 10.1109/JAS.2023.123033

Citation:

Z. B. Wei, H. X. Zhao, Z. S. Li, X. J. Bu, Y. Y. Chen, X. Q. Zhang, Y. S. Lv, and F.-Y. Wang, “STGSA: A novel spatial-temporal graph synchronous aggregation model for traffic prediction,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 1, pp. 226–238, Jan. 2023. doi: 10.1109/JAS.2023.123033

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STGSA: A Novel Spatial-Temporal Graph Synchronous Aggregation Model for Traffic Prediction

doi: 10.1109/JAS.2023.123033

Zebing Wei^{1, 2},
Hongxia Zhao¹,
Zhishuai Li^{1, 2},
Xiaojie Bu⁴,
Yuanyuan Chen¹,
Xiqiao Zhang^{5
,
,},
Yisheng Lv^{1, 3
,
,},
Fei-Yue Wang¹

1.
State Key Laboratory for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
2.
School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
3.
Shandong Key Laboratory of Smart Transportation (Preparation), Shandong Jiao Tong University, Shanghai 200240, China
4.
College of Science, China University of Petroleum, Qingdao 266580, China
5.
Department of Transportation Engineering, School of Transportation Science and Technology, Harbin Institute of Technology, Harbin 150090, China

Funds: This work was partially supported by the National Key Research and Development Program of China (2020YFB2104001)

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Author Bio:
Zebing Wei received the B.E. degree in automation from Dalian University of Technology, in 2019, and the MA. Eng. degree at the School of Artificial Intelligence, University of Chinese Academy of Sciences as well as the State Key Laboratory for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences, in 2022. His research interest includes intelligent transportation and big data mining

Hongxia Zhao received the Ph.D. degree in control theory and control engineering from the Institute of Automation, Chinese Academy of Sciences, in 2009. She is currently working as an Assistant Researcher with The State Key Laboratory for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences. Her research interests include intelligent control, data analysis and display. She is a Member of the IEEE Intelligent Transportation Systems Society. She has been involved in the organization of many IEEE international conferences

Zhishuai Li received the B.E. degree in automation from China University of Petroleum, in 2017. He is currently pursuing the Ph.D. degree at the State Key Laboratory for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences as well as the School of Artificial Intelligence, University of Chinese Academy of Sciences. His research interests include intelligent optimization algorithms and intelligent transportation systems

Xiaojie Bu received the B.S. degree in mathematics and applied mathematics from the Central South University, in 2017. She is currently pursuing the M.A.S degree with the College of Science, China University of Petroleum, as well as the State Key Laboratory for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences. Her research interests include intelligent transportation and multivariate statistical analysis

Yuanyuan Chen received the B.E. degree in automation from Tongji University, in 2013, and the Ph.D. degree in control theory and control engineering from the University of Chinese Academy of Sciences, in 2018. He is currently an Assistant Professor with the Institute of Automation, Chinese Academy of Sciences. His research interests include social transportation systems, and data-driven traffic modeling and prediction

Xiqiao Zhang received the B.E. degree in traffic engineering, the M.E. degree in transportation planning and management, and the Ph.D. degree in bridge and tunnel engineering from Harbin Institute of Technology, in 2001, 2004, and 2008, respectively. He is currently an Associate Professor with the Department of Transportation Engineering, School of Transportation Science and Technology, Harbin Institute of Technology. His research interests include traffic data analysis, traffic control, dynamic traffic modeling

Yisheng Lv (Senior Member, IEEE) received the B.E. and M.E. degrees in transportation engineering from Harbin Institute of Technology, in 2005 and 2007, respectively, and the Ph.D. degree in control theory and control engineering from the Chinese Academy of Sciences, in 2010. He is a Professor with the State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences. His research interests include traffic data analysis, dynamic traffic modeling, and parallel traffic management and control systems

Fei-Yue Wang (Fellow, IEEE) joined the University of Arizona in 1990 and became a Professor and Director of the Robotics and Automation Laboratory (RAL) and Program in Advanced Research for Complex Systems (PARCS). In 1999, he founded the Intelligent Control and Systems Engineering Center at the Institute of Automation, Chinese Academy of Sciences (CAS), and was appointed as the Director of the Key Lab of Complex Systems and Intelligence Science, CAS. From 2006 to 2010, he was Vice President for Research, Education, and Academic Exchanges at the Institute of Automation, CAS. In 2011, he became the State Specially Appointed Expert and the Director of the State Key Laboratory for Management and Control of Complex Systems.Dr. Wang’s current research focuses on methods and applications for parallel systems, social computing, parallel intelligence and knowledge automation. He was the Founding Editor-in-Chief of the International Journal of Intelligent Control and Systems (1995–2000), Founding EiC of IEEE ITS Magazine (2006–2007), EiC of IEEE Intelligent Systems (2009–2012), and EiC of IEEE Transactions on ITS (2009–2016). Currently he is EiC of IEEE Transactions on Computational Social Systems, Founding EiC of IEEE/CAA Journal of Automatica Sinica, and Chinese Journal of Command and Control. Since 1997, he has served as General or Program Chair of more than 20 IEEE, INFORMS, ACM, and ASME conferences. He was the President of IEEE ITS Society (2005–2007), Chinese Association for Science and Technology (CAST, USA) in 2005, the American Zhu Kezhen Education Foundation (2007-2008), the Vice President of the ACM China Council (2010–2011), and the Vice President and Secretary General of Chinese Association of Automation (CAA, 2008–2018). Currently he is President of CAA Supervision Council, President of IEEE Council on RFID and Vice President of IEEE SMC Society
Corresponding author: Xiqiao Zhang, e-mail: zxqjuly@hit.edu.cn; Yisheng Lv, e-mail: yisheng.lv@ia.ac.cn
Received Date: 2022-06-29
Revised Date: 2022-08-07
Accepted Date: 2022-08-20

Available Online: 2022-11-11

Abstract

Abstract

The success of intelligent transportation systems relies heavily on accurate traffic prediction, in which how to model the underlying spatial-temporal information from traffic data has come under the spotlight. Most existing frameworks typically utilize separate modules for spatial and temporal correlations modeling. However, this stepwise pattern may limit the effectiveness and efficiency in spatial-temporal feature extraction and cause the overlook of important information in some steps. Furthermore, it is lacking sufficient guidance from prior information while modeling based on a given spatial adjacency graph (e.g., deriving from the geodesic distance or approximate connectivity), and may not reflect the actual interaction between nodes. To overcome those limitations, our paper proposes a spatial-temporal graph synchronous aggregation (STGSA) model to extract the localized and long-term spatial-temporal dependencies simultaneously. Specifically, a tailored graph aggregation method in the vertex domain is designed to extract spatial and temporal features in one graph convolution process. In each STGSA block, we devise a directed temporal correlation graph to represent the localized and long-term dependencies between nodes, and the potential temporal dependence is further fine-tuned by an adaptive weighting operation. Meanwhile, we construct an elaborated spatial adjacency matrix to represent the road sensor graph by considering both physical distance and node similarity in a data-driven manner. Then, inspired by the multi-head attention mechanism which can jointly emphasize information from different representation subspaces, we construct a multi-stream module based on the STGSA blocks to capture global information. It projects the embedding input repeatedly with multiple different channels. Finally, the predicted values are generated by stacking several multi-stream modules. Extensive experiments are constructed on six real-world datasets, and numerical results show that the proposed STGSA model significantly outperforms the benchmarks.
- Deep learning,
- graph neural network (GNN),
- multi-stream,
- spatial-temporal feature extraction,
- temporal graph,
- traffic prediction

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References(52)

References

[1]	F.-Y. Wang, “Parallel control and management for intelligent transportation systems: Concepts, architectures, and applications,” IEEE Trans. Intelligent Transportation Systems, vol. 11, no. 3, pp. 630–638, 2010. doi: 10.1109/TITS.2010.2060218
[2]	J. P. Zhang, F.-Y. Wang, K. F. Wang, W.-H. Lin, X. Xu, and C. Chen, “Data-driven intelligent transportation systems: A survey,” IEEE Trans. Intelligent Transportation Systems, vol. 12, no. 4, pp. 1624–1639, 2011. doi: 10.1109/TITS.2011.2158001
[3]	F.-Y. Wang, “Scanning the issue and beyond: Computational transportation and transportation 5.0,” IEEE Trans. Intelligent Transportation Systems, vol. 5, no. 15, pp. 1861–1868, 2014.
[4]	M. Veres and M. Moussa, “Deep learning for intelligent transportation systems: A survey of emerging trends,” IEEE Trans. Intelligent Transportation Systems, vol. 21, no. 8, pp. 3152–3168, 2019.
[5]	N. Zhang, F.-Y. Wang, F. H. Zhu, D. B. Zhao, and S. M. Tang, “Dynacas: Computational experiments and decision support for its,” IEEE Intelligent Systems, vol. 23, no. 6, pp. 19–23, 2008. doi: 10.1109/MIS.2008.101
[6]	Y. Lee, H. Jeon, and K. Sohn, “Predicting short-term traffic speed using a deep neural network to accommodate citywide spatio-temporal correlations,” IEEE Trans. Intelligent Transportation Systems, vol. 22, no. 3, pp. 1435–1448, 2021. doi: 10.1109/TITS.2020.2970754
[7]	Z. S. Li, G. Xiong, Y. L. Tian, Y. S. Lv, Y. Y. Chen, P Hui, and X. Su, “A multi-stream feature fusion approach for traffic prediction,” IEEE Trans. Intelligent Transportation Systems, vol. 23, no. 2, pp. 1456–1466, Feb. 2022.
[8]	C. Song, Y. F. Lin, S. N. Guo, and H. Y. Wan, “Spatial-temporal synchronous graph convolutional networks: A new framework for spatial-temporal network data forecasting,” in Proc. AAAI Conf. Artificial Intelligence, 2020, pp. 914–921.
[9]	Y. S. Lv, Y. J. Duan, W. W. Kang, Z. X. Li, and F.-Y. Wang, “Traffic flow prediction with big data: A deep learning approach,” IEEE Trans. Intelligent Transportation Systems, vol. 16, no. 2, pp. 865–873, 2014.
[10]	N. G. Polson and V. O. Sokolov, “Deep learning for short-term traffic flow prediction,” Transportation Research Part C: Emerging Technologies, vol. 79, pp. 1–17, 2017. doi: 10.1016/j.trc.2017.02.024
[11]	Y. G. Li, R. Yu, C. Shahabi, and Y. Liu, “Diffusion convolutional recurrent neural network: Data-driven traffic forecasting,” in Proc. Int. Conf. Learning Representations, 2018.
[12]	B. Yu, H. Yin, and Z. Zhu, “Spatio-temporal graph convolutional networks: a deep learning framework for traffic forecasting,” in Proc. 27th Int. Joint Conf. Artificial Intelligence, 2018, pp. 3634–3640.
[13]	A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. Kaiser, and I. Polosukhin, “Attention is all you need,” in NIPS, 2017.
[14]	J. H. Guo, W. Huang, and B. M. Williams, “Adaptive kalman filter approach for stochastic short-term traffic flow rate prediction and uncertainty quantification,” Transportation Research Part C: Emerging Technologies, vol. 43, pp. 50–64, 2014. doi: 10.1016/j.trc.2014.02.006
[15]	M. Van Der Voort, M. Dougherty, and S. Watson, “Combining kohonen maps with arima time series models to forecast traffic flow,” Transportation Research Part C: Emerging Technologies, vol. 4, no. 5, pp. 307–318, 1996. doi: 10.1016/S0968-090X(97)82903-8
[16]	R. M. Li, C. Y. Jiang, F. H. Zhu, and X. L. Chen, “Traffic flow data forecasting based on interval type-2 fuzzy sets theory,” IEEE/CAA J. Autom. Sinica, vol. 3, no. 2, pp. 141–148, 2016. doi: 10.1109/JAS.2016.7451101
[17]	R. M. Li, Y. F. Huang, and J. Wang, “Long-term traffic volume prediction based on k-means gaussian interval type-2 fuzzy sets,” IEEE/CAA J. Autom. Sinica, vol. 6, no. 6, pp. 1344–1351, 2019.
[18]	L. Li, X. Q. Chen, and L. Zhang, “Multimodel ensemble for freeway traffic state estimations,” IEEE Trans. Intelligent Transportation Systems, vol. 15, no. 3, pp. 1323–1336, 2014. doi: 10.1109/TITS.2014.2299542
[19]	V. Le Guen and N. Thome, “Probabilistic time series forecasting with shape and temporal diversity,” Advances in Neural Information Processing Systems, vol. 33, 2020.
[20]	J. Schmidhuber and S. Hochreiter, “Long short-term memory,” Neural Comput, vol. 9, no. 8, pp. 1735–1780, 1997. doi: 10.1162/neco.1997.9.8.1735
[21]	R. Dai, S. K. Xu, Q. Gu, C. G. Ji, and K. K. Liu, “Hybrid spatio-temporal graph convolutional network: Improving traffic prediction with navigation data,” in Proc. 26th ACM SIGKDD Int. Conf. Knowledge Discovery & Data Mining, 2020, pp. 3074–3082.
[22]	Y. N. Dauphin, A. Fan, M. Auli, and D. Grangier, “Language modeling with gated convolutional networks,” in Proc. Int. Conf. Machine Learning. PMLR, 2017, pp. 933–941.
[23]	Z. N. Yuan, X. Zhou, and T. B. Yang, “Hetero-ConvLSTM: A deep learning approach to traffic accident prediction on heterogeneous spatio-temporal data,” in Proc. 24th ACM SIGKDD Int. Conf. Knowledge Discovery and Data Mining, 2018, pp. 984–992.
[24]	Y. Seo, M. Defferrard, P. Vandergheynst, and X. Bresson, “Structured sequence modeling with graph convolutional recurrent networks,” in Proc. Int. Conf. Neural Information Processing, 2018, pp. 362–373.
[25]	Y. Y. Chen, H. Y. Chen, P. J. Ye, Y. S. Lv, and F.-Y. Wang, “Acting as a decision maker: Traffic-condition-aware ensemble learning for traffic flow prediction,” IEEE Trans. Intelligent Transportation Systems, vol. 23, no. 4, pp. 1–11, 2020.
[26]	Y. L. Lin, X. Y. Dai, L. Li, and F.-Y. Wang, “Pattern sensitive prediction of traffic flow based on generative adversarial framework,” IEEE Trans. Intelligent Transportation Systems, vol. 20, no. 6, pp. 2395–2400, 2018.
[27]	S. Y. Hao, D.-H. Lee, and D. Zhao, “Sequence to sequence learning with attention mechanism for short-term passenger flow prediction in large-scale metro system,” Transportation Research Part C: Emerging Technologies, vol. 107, pp. 287–300, 2019. doi: 10.1016/j.trc.2019.08.005
[28]	L. Cai, K. Janowicz, G. C. Mai, B. Yan, and R. Zhu, “Traffic transformer: Capturing the continuity and periodicity of time series for traffic forecasting,” Transactions in GIS, vol. 24, no. 3, pp. 736–755, 2020. doi: 10.1111/tgis.12644
[29]	Z. Y. Pan, Y. X. Liang, W. F. Wang, Y. Yu, Y. Zheng, and J. B. Zhang, “Urban traffic prediction from spatio-temporal data using deep meta learning,” in Proc. 25th ACM SIGKDD Int. Conf. Knowledge Discovery and Data Mining, 2019, pp. 1720–1730.
[30]	J. B. Estrach, W. Zaremba, A. Szlam, and Y. LeCun, “Spectral networks and deep locally connected networks on graphs,” in Proc. 2nd Int. Conf. Learning Representations, ICLR, 2014.
[31]	M. Defferrard, X. Bresson, and P. Vandergheynst, “Convolutional neural networks on graphs with fast localized spectral filtering,” in NIPS, 2016.
[32]	T. N. Kipf and M. Welling, “Semi-supervised classification with graph convolutional networks,” arXiv preprint arXiv: 1609.02907, 2016.
[33]	W. L. Hamilton, R. Ying, and J. Leskovec, “Inductive representation learning on large graphs,” in Proc. 31st Int. Conf. Neural Information Processing Systems, 2017, pp. 1025–1035.
[34]	J. Atwood and D. Towsley, “Diffusion-convolutional neural networks,” in Advances in Neural Information Processing Systems, 2016, pp. 1993–2001.
[35]	P. Velickovic, G. Cucurull, A. Casanova, A. Romero, P. Lio, and Y. Bengio, “Graph attention networks,” in Proc. 7th Int. Conf. Learning Representations, 2018.
[36]	X. Y. Wang, Y. Ma, Y. Q. Wang, W. Jin, X. Wang, J. L. Tang, C. Y. Jia, and J. Yu, “Traffic flow prediction via spatial temporal graph neural network,” in Proc. Web Conf., 2020, pp. 1082–1092.
[37]	G. Q. Liu, Y. L. Wu, D. Zhao, and H. C. Zhou, “Time-adaptive graph convolutional network for traffic prediction,” in Proc. 5th Int. Conf. Deep Learning Technologies, 2021, pp. 81–86.
[38]	W. Zhang, F. H. Zhu, Y. S. Lv, C Tan, W. Liu, X. Zhang, and F.-Y. Wang “AdapGL: An adaptive graph learning algorithm for traffic prediction based on spatiotemporal neural networks,” Transportation Research Part C: Emerging Technologies, vol. 139, p. 103659, 2022.
[39]	A. T. Khan, X. W. Cao, Z. Li, and S. Li, “Enhanced beetle antennae search with zeroing neural network for online solution of constrained optimization,” Neurocomputing, vol. 447, pp. 294–306, 2021. doi: 10.1016/j.neucom.2021.03.027
[40]	A. T. Khan, S. Li, and X. W. Cao, “Human guided cooperative robotic agents in smart home using beetle antennae search,” Science China Information Sciences, vol. 65, no. 2, pp. 1–17, 2022.
[41]	A. T. Khan, S. Li, and Z. Li, “Obstacle avoidance and model-free tracking control for home automation using bio-inspired approach,” Advanced Control for Applications: Engineering and Industrial Systems, vol. 4, no. 1, p. e63, 2022.
[42]	Z. C. Zhang, X. Lin, M. Li, and Y. H. Wang, “A customized deep learning approach to integrate network-scale online traffic data imputation and prediction,” Transportation Research Part C: Emerging Technologies, vol. 132, p. 103372, 2021. doi: 10.1016/j.trc.2021.103372
[43]	D. Wu and X. Luo, “Robust latent factor analysis for precise representation of high-dimensional and sparse data,” IEEE/CAA J. Autom. Sinica, vol. 8, no. 4, pp. 796–805, 2020.
[44]	H. Wu, X. Luo, and M. C. Zhou, “Advancing non-negative latent factorization of tensors with diversified regularizations,” IEEE Trans. Services Computing, vol. 15, no. 3, pp. 1334–1344, 2020.
[45]	X. Luo, Y. Yuan, S. L. Chen, N. Y. Zeng, and Z. D. Wang, “Position-transitional particle swarm optimization-incorporated latent factor analysis,” IEEE Trans. Knowledge and Data Engineering, vol. 34, no. 8, pp. 3958–3970, 2022.
[46]	X. Geng, Y. G. Li, L. Y. Wang, L. Y. Zhang, Q. Yang, J. P. Ye, and Y. Liu, “Spatiotemporal multi-graph convolution network for ride-hailing demand forecasting,” in Proc. AAAI Conf. Artificial Intelligence, 2019, pp. 3656–3663.
[47]	R. Y. Li, S. Wang, F. Y. Zhu, and J. Z. Huang, “Adaptive graph convolutional neural networks,” in Proc. AAAI Conf. Artificial Intelligence, 2018.
[48]	Z. H. Wu, S. R. Pan, G. D. Long, J. Jiang, and C. Q. Zhang, “Graph WaveNet for deep spatial-temporal graph modeling.” in Proc. 28th Int. Joint Conf. Artificial Intelligence, 2019, pp. 1907–1913.
[49]	C. P. Zheng, X. L. Fan, C. Wang, and J. Z. Qi, “GMAN: A graph multi-attention network for traffic prediction,” in Proc. AAAI Conf. Artificial Intelligence, 2020, pp. 1234–1241.
[50]	L. Bai, L. N. Yao, C. Li, X. Z. Wang, and C. Wang, “Adaptive graph convolutional recurrent network for traffic forecasting,” Advances in Neural Information Processing Systems, vol. 33, 2020.
[51]	M. Z. Li and Z. X. Zhu “Spatial-temporal fusion graph neural networks for traffic flow forecasting,” arXiv preprint arXiv: 2012.09641, 2020.
[52]	I. Sutskever, O. Vinyals, and Q. V. Le, “Sequence to sequence learning with neural networks,” Advances in Neural Information Processing Systems, vol. 27, pp. 3104–3112, 2014.

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This paper proposed a spatial-temporal graph synchronous aggregation model for traffic prediction. It can model both the localized and long-term spatial-temporal dependency directly, and extract the hidden spatial-temporal features in one step without additional modules
The proposed method can capture more trends of relevant nodes by implementing the heuristic spatial adjacency matrix optimization algorithm which makes it contain more useful information
The designed temporal graph is easily to be constructed, and the overall performance is significantly improved

STGSA: A Novel Spatial-Temporal Graph Synchronous Aggregation Model for Traffic Prediction

doi: 10.1109/JAS.2023.123033

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