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Recent studies have shifted their focus towards formulating traffic forecasting as a spatio-temporal graph modeling problem.
J. D. Hamilton, Time series analysis . Princeton university press, 1994
1994
Earlier work this paper cites.
M. Schuster and K. K. Paliwal, “Bidirectional recurrent neural networks,” IEEE Transactions on Signal Processing , vol. 45, no. 11, pp. 2673–2681, 1997
1997
Earlier work this paper cites.
H. Drucker, C. J. Burges, L. Kaufman, A. Smola, and V. Vapoik, “Support vector regression machines,” in NeurIPS , 1997, pp. 155–161
1997
Earlier work this paper cites.
V. Nair and G. E. Hinton, “Rectified linear units improve restricted boltzmann machines,” in ICML , 2010, pp. 807–814
2010
Earlier work this paper cites.
G. Carlsson and V. de Silva, “Zigzag persistence,” Foundations of computational mathematics , vol. 10, pp. 367–405, 2010
2012
Earlier work this paper cites.
J. Bruna, W. Zaremba, A. Szlam, and Y. LeCun, “Spectral networks and deep locally connected networks on graphs,” in ICLR , 2014
2014
Earlier work this paper cites.
I. Sutskever, O. Vinyals, and Q. V. Le, “Sequence to sequence learning with neural networks,” in NeurIPS , 2014, pp. 3104–3112
2014
Earlier work this paper cites.
X. Ma, Z. Tao, Y. Wang, H. Yu, and Y. Wang, “Long short-term memory neural network for traffic speed prediction using remote microwave sensor data,” Transportation Research Part C: Emerging Technologies , vol. 54, pp. 187–197, 2015
2015
Earlier work this paper cites.
S. Ioffe and C. Szegedy, “Batch normalization: Accelerating deep network training by reducing internal covariate shift,” in ICML , 2015, pp. 448–456
2015
Earlier work this paper cites.
D. P. Kingma and J. L. Ba, “Adam: a method for stochastic optimization,” in ICLR , 2015
2015
Earlier work this paper cites.
M. Defferrard, X. Bresson, and P. Vandergheynst, “Convolutional neural networks on graphs with fast localized spectral filtering,” in NeurIPS , 2016, pp. 3844–3852
2016
Earlier work this paper cites.
D. Deng, C. Shahabi, U. Demiryurek, L. Zhu, R. Yu, and Y. Liu, “Latent space model for road networks to predict time-varying traffic,” in KDD , 2016
2016
Earlier work this paper cites.
2016
Earlier work this paper cites.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in CVPR , 2016, pp. 770–778
2016
Earlier work this paper cites.
T. N. Kipf and M. Welling, “Semi-supervised classification with graph convolutional networks,” in ICLR , 2017
2017
Earlier work this paper cites.
M. M. Bronstein, J. Bruna, Y. LeCun, A. Szlam, and P. Vandergheynst, “Geometric deep learning: going beyond euclidean data,” IEEE Signal Processing Magazine , vol. 34, no. 4, pp. 18–42, 2017
2017
Earlier work this paper cites.
W. L. Hamilton, R. Ying, and J. Leskovec, “Inductive representation learning on large graphs,” in NeurIPS , 2017, pp. 1024–1034
2017
Earlier work this paper cites.
J. Zhang, Y. Zheng, and D. Qi, “Deep spatio-temporal residual networks for citywide crowd flows prediction,” in AAAI , 2017, pp. 1655–1661
2017
Earlier work this paper cites.
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Łukasz Kaiser, and I. Polosukhin, “Attention is all you need,” in NeurIPS , 2017, pp. 5998–6008
2017
Earlier work this paper cites.
Y. Li, R. Yu, C. Shahabi, and Y. Liu, “Diffusion convolutional recurrent neural network: Data-driven traffic forecasting,” in ICLR , 2018
2018
Earlier work this paper cites.
W. Cheng, Y. Shen, Y. Zhu, and L. Huang, “A neural attention model for urban air quality inference: learning the weights of monitoring stations,” in AAAI , 2018, pp. 2151–2158
2018
Cited alongside, same era.
B. Yu, H. Yin, and Z. Zhu, “Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting,” in IJCAI , 2018, pp. 3634–3640
2018
Cited alongside, same era.
M. Zhang and Y. Chen, “Link prediction based on graph neural networks,” in NeurIPS , 2018, pp. 5165–5175
2018
Cited alongside, same era.
P. Veličković, G. Cucurull, A. Casanova, A. Romero, P. Liò, and Y. Bengio, “Graph attention networks,” in ICLR , 2018
2018
Cited alongside, same era.
S. Yan, Y. Xiong, and D. Lin, “Spatial temporal graph convolutional networks for skeleton-based action recognition,” in AAAI , 2018, p. 3482–3489
2018
2020
Later among the works it cites.
J. Sun, J. Zhang, Q. Li, X. Yi, Y. Liang, and Y. Zheng, “Predicting citywide crowd flows in irregular regions using multi-view graph convolutional networks,” IEEE Transactions on Knowledge and Data Engineering , 2020
2020
Later among the works it cites.
J. Gu, Q. Zhou, J. Yang, Y. Liu, F. Zhuang, Y. Zhao, and H. Xiong, “Exploiting interpretable patterns for flow prediction in dockless bike sharing systems,” IEEE Transactions on Knowledge and Data Engineering , 2020
2020
Later among the works it cites.
C. Zheng, X. Fan, C. Wen, L. Chen, C. Wang, and J. Li, “Deepstd: Mining spatio-temporal disturbances of multiple context factors for citywide traffic flow prediction,” IEEE Transactions on Intelligent Transportation Systems , vol. 21, no. 9, pp. 3744–3755, 2020
2020
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Cited alongside, same era.
H. Yao, F. Wu, J. Ke, X. Tang, Y. Jia, S. Lu, P. Gong, J. Ye, and Z. Li, “Deep multi-view spatial-temporal network for taxi demand prediction,” in AAAI , 2018, pp. 2588–2595
2018
Cited alongside, same era.
2018
Cited alongside, same era.
2018
Cited alongside, same era.
Z. Wu, S. Pan, G. Long, J. Jiang, and C. Zhang, “Graph wavenet for deep spatial-temporal graph modeling,” in IJCAI , 2019
2019
Cited alongside, same era.
S. Guo, Y. Lin, N. Feng, C. Song, and HuaiyuWan, “Attention based spatial-temporal graph convolutional networks for traffic flow forecasting,” in AAAI , 2019, pp. 922–929
2019
Cited alongside, same era.
L. Shi, Y. Zhang, J. Cheng, and H. Lu, “Two-stream adaptive graph convolutional networks for skeleton-based action recognition,” in CVPR , 2019, p. 12026–12035
2019
Cited alongside, same era.
H. Yao, X. Tang, H. Wei, G. Zheng, and Z. Li, “Revisiting spatial-temporal similarity: A deep learning framework for traffic prediction,” in AAAI , 2019
2019
Cited alongside, same era.
Later among the works it cites.
J. Zhang, Y. Zheng, J. Sun, and D. Qi, “Flow prediction in spatio-temporal networks based on multitask deep learning,” IEEE Transactions on Knowledge and Data Engineering , vol. 32, no. 3, 2020
2020
Later among the works it cites.
2020
Later among the works it cites.
C. Zheng, X. Fan, C. Wang, and J. Qi, “Gman: A graph multi-attention network for traffic prediction,” in AAAI , 2020, pp. 1234–1241
2020
Later among the works it cites.
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 WWW , 2020, pp. 1082–1092
2020
Later among the works it cites.
Y.-J. Lu and C.-T. Li, “Agstn: Learning attention-adjusted graph spatio-temporal networks for short-term urban sensor value forecasting,” in 2020 IEEE International Conference on Data Mining (ICDM) . IEEE, 2020, pp. 1148–1153
2020
Later among the works it cites.
W. Chen, L. Chen, Y. Xie, W. Cao, Y. Gao, and X. Feng, “Multi-range attentive bicomponent graph convolutional network for traffic forecasting,” in AAAI , 2020
2020
Later among the works it cites.
C. Zheng, C. Wang, X. Fan, J. Qi, and X. Yan, “Stpc-net: Learn massive geo-sensory data as spatio-temporal point clouds,” IEEE Transactions on Intelligent Transportation Systems , 2021
2021
Closest in time.
Y. Chen, I. Segovia-Dominguez, and Y. R. Gel, “Z-gcnets: Time zigzags at graph convolutional networks for time series forecasting,” in ICML , 2021
2021
Closest in time.
Z. Wu, S. Pan, F. Chen, G. Long, C. Zhang, and P. S. Yu, “A comprehensive survey on graph neural networks,” IEEE Transactions on Neural Networks and Learning Systems , vol. 32, no. 1, pp. 4–24, 2021
2021
Closest in time.
S. Zhang, Y. Guo, P. Zhao, C. Zheng, and X. Chen, “A graph-based temporal attention framework for multi-sensor traffic flow forecasting,” IEEE Transactions on Intelligent Transportation Systems , 2021
2021
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R. Jiang, Z. Cai, Z. Wang, C. Yang, Z. Fan, Q. Chen, K. Tsubouchi, X. Song, and R. Shibasaki, “Deepcrowd: A deep model for large-scale citywide crowd density and flow prediction,” IEEE Transactions on Knowledge and Data Engineering , 2021
2021
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S. Guo, Y. Lin, H. Wan, X. Li, and G. Cong, “Learning dynamics and heterogeneity of spatial-temporal graph data for traffic forecasting,” IEEE Transactions on Knowledge and Data Engineering , 2021
2021
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Y. H. Lau and R. C.-W. Wong, “Spatio-temporal graph convolutional networks for traffic forecasting: Spatial layers first or temporal layers first?” in Proceedings of the 29th International Conference on Advances in Geographic Information Systems , 2021, pp. 427–430
2021
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2022
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M. Niepert, M. Ahmed, and K. Kutzkov, “Learning convolutional neural networks for graphs,” in ICML , 2016, pp. 2014–2023
2023
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