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In traffic forecasting, graph convolutional networks (GCNs), which model traffic flows as spatio-temporal graphs, have achieved remarkable performance.
M. S. Ahmed and A. R. Cook, Analysis of freeway traffic time-series data by using Box-Jenkins techniques , 1979, no. 722
1979
Earlier work this paper cites.
I. Okutani and Y. J. Stephanedes, “Dynamic prediction of traffic volume through kalman filtering theory,” Transportation Research Part B: Methodological , vol. 18, no. 1, pp. 1–11, 1984
1984
Earlier work this paper cites.
D. Park and L. R. Rilett, “Forecasting freeway link travel times with a multilayer feedforward neural network,” Computer-Aided Civil and Infrastructure Engineering , vol. 14, no. 5, pp. 357–367, 1999
1999
Earlier work this paper cites.
B. M. Williams and L. A. Hoel, “Modeling and forecasting vehicular traffic flow as a seasonal arima process: Theoretical basis and empirical results,” Journal of transportation engineering , vol. 129, no. 6, pp. 664–672, 2003
2003
Earlier work this paper cites.
C.-H. Wu, J.-M. Ho, and D.-T. Lee, “Travel-time prediction with support vector regression,” IEEE transactions on intelligent transportation systems , vol. 5, no. 4, pp. 276–281, 2004
2004
Earlier work this paper cites.
J. Liu and W. Guan, “A summary of traffic flow forecasting methods [j],” Journal of Highway and Transportation Research and Development , vol. 3, pp. 82–85, 2004
2004
Earlier work this paper cites.
A. J. Smola and B. Schölkopf, “A tutorial on support vector regression,” Statistics and computing , vol. 14, no. 3, pp. 199–222, 2004
2004
Earlier work this paper cites.
G. Leshem and Y. Ritov, “Traffic flow prediction using adaboost algorithm with random forests as a weak learner,” in Proceedings of world academy of science, engineering and technology , vol. 19. Citeseer, 2007, pp. 193–198
2007
Earlier work this paper cites.
F. Guerrini, “Traffic congestion costs americans $124 billion a year, report says,” Forbes, October , vol. 14, 2014
2014
Earlier work this paper cites.
Y. Lv, Y. Duan, W. Kang, Z. Li, and F.-Y. Wang, “Traffic flow prediction with big data: a deep learning approach,” IEEE Transactions on Intelligent Transportation Systems , vol. 16, no. 2, pp. 865–873, 2014
2014
Earlier work this paper cites.
2014
Earlier work this paper cites.
2014
Cited alongside, same era.
2016
Cited alongside, same era.
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
2016
Cited alongside, same era.
M. Defferrard, X. Bresson, and P. Vandergheynst, “Convolutional neural networks on graphs with fast localized spectral filtering,” in Advances in neural information processing systems , 2016, pp. 3844–3852
2016
Cited alongside, same era.
2017
Later among the works it cites.
2017
Later among the works it cites.
S. Yan, Y. Xiong, and D. Lin, “Spatial temporal graph convolutional networks for skeleton-based action recognition,” in Thirty-second AAAI conference on artificial intelligence , 2018
2018
Later among the works it cites.
L. Zhao, Y. Song, C. Zhang, Y. Liu, P. Wang, T. Lin, M. Deng, and H. Li, “T-gcn: A temporal graph convolutional network for traffic prediction,” IEEE Transactions on Intelligent Transportation Systems , 2019
2019
Later among the works it cites.
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Y. Gal and Z. Ghahramani, “Dropout as a bayesian approximation: Representing model uncertainty in deep learning,” in international conference on machine learning , 2016, pp. 1050–1059
2016
Cited alongside, same era.
T. N. Kipf and M. Welling, “Variational graph auto-encoders,” arXiv preprint arXiv:1611.07308 , 2016
2016
Cited alongside, same era.
J. Zhang, Y. Zheng, and D. Qi, “Deep spatio-temporal residual networks for citywide crowd flows prediction,” in Thirty-First AAAI Conference on Artificial Intelligence , 2017
2017
Cited alongside, same era.
H. Yu, Z. Wu, S. Wang, Y. Wang, and X. Ma, “Spatiotemporal recurrent convolutional networks for traffic prediction in transportation networks,” Sensors , vol. 17, no. 7, p. 1501, 2017
2017
Cited alongside, same era.
2017
Cited alongside, same era.
A. Paszke, S. Gross, S. Chintala, G. Chanan, E. Yang, Z. DeVito, Z. Lin, A. Desmaison, L. Antiga, and A. Lerer, “Automatic differentiation in pytorch,” 2017
2017
Cited alongside, same era.
B. Jiang, Z. Zhang, D. Lin, J. Tang, and B. Luo, “Semi-supervised learning with graph learning-convolutional networks,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2019, pp. 11 313–11 320
2019
Later among the works it cites.
Y. Zhang, S. Pal, M. Coates, and D. Ustebay, “Bayesian graph convolutional neural networks for semi-supervised classification,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 33, 2019, pp. 5829–5836
2019
Later among the works it cites.
2019
Later among the works it cites.
Y. Lu, L. Liu, J. Panneerselvam, B. Yuan, J. Gu, and N. Antonopoulos, “A gru-based prediction framework for intelligent resource management at cloud data centres in the age of 5g,” IEEE Transactions on Cognitive Communications and Networking , 2019
2019
Later among the works it cites.
2020
Closest in time.