Dynamic traveling time forecasting based on spatial-temporal graph convolutional networks

Fangshu CHEN , Yufei ZHANG , Lu CHEN , Xiankai MENG , Yanqiang QI , Jiahui WANG

Front. Comput. Sci. ›› 2023, Vol. 17 ›› Issue (6) : 176615

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Front. Comput. Sci. ›› 2023, Vol. 17 ›› Issue (6) : 176615 DOI: 10.1007/s11704-023-2704-x
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Dynamic traveling time forecasting based on spatial-temporal graph convolutional networks

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Fangshu CHEN, Yufei ZHANG, Lu CHEN, Xiankai MENG, Yanqiang QI, Jiahui WANG. Dynamic traveling time forecasting based on spatial-temporal graph convolutional networks. Front. Comput. Sci., 2023, 17(6): 176615 DOI:10.1007/s11704-023-2704-x

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References

[1]

Dai G, Hu X, Ge Y, Ning Z, Liu Y . Attention based simplified deep residual network for citywide crowd flows prediction. Frontiers of Computer Science, 2021, 15( 2): 152317

[2]

Chen F, Qi Y, Wang J, Chen L, Zhang Y, Shi L . Temporal metrics based aggregated graph convolution network for traffic forecasting. Neurocomputing, 2023, 556: 126662

[3]

Pedersen S A, Yang B, Jensen C S . Fast stochastic routing under time-varying uncertainty. The VLDB Journal, 2020, 29( 4): 819–839

[4]

Guo C, Yang B, Hu J, Jensen C. Learning to route with sparse trajectory sets. In: Proceeding of the 34th IEEE International Conference on Data Engineering. 2018, 1073−1084

[5]

Li Y, Yu R, Shahabi C, Liu Y. Diffusion convolutional recurrent neural network: Data-driven traffic forecasting. In: Proceedings of the 6th International Conference on Learning Representations. 2018, 1−16

[6]

Wu Z, Pan S, Long G, Jiang J, Zhang C. Graph wavenet for deep spatial-temporal graph modeling. In: Proceedings of the 28th International Joint Conference on Artificial Intelligence. 2019, 1907−1913

[7]

Seo Y, Defferrard M, Vandergheynst P, Bresson X. Structured sequence modeling with graph convolutional recurrent networks. In: Proceedings of the 25th International Conference on Neural Information Processing. 2018, 362−373

[8]

Yu B, Yin H, Zhu Z. Spatio-temporal graph convolutional networks: a deep learning framework for traffic forecasting. In: Proceedings of the 27th International Joint Conference on Artificial Intelligence. 2018, 3634−3640

[9]

Guo K, Hu Y, Qian Z, Liu H, Zhang K, Sun Y, Gao J, Yin B . Optimized graph convolution recurrent neural network for traffic prediction. IEEE Transactions on Intelligent Transportation Systems, 2021, 22( 2): 1138–1149

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