Spatial-Temporal Bipartite Graph Attention Network for Traffic Forecasting

Author:

Lakmal Dimuthu,Perera Kushani,Borovica-Gajic Renata,Karunasekera Shanika

Publisher

Springer Nature Singapore

Reference29 articles.

1. Bai, L., Yao, L., Kanhere, S.S., Wang, X., Liu, W., Yang, Z.: Spatio-temporal graph convolutional and recurrent networks for citywide passenger demand prediction. In: Proceedings of the 28th ACM CIKM, pp. 2293–2296 (2019)

2. Chen, C., Petty, K., Skabardonis, A., Varaiya, P., Jia, Z.: Freeway performance measurement system: mining loop detector data. TRR 1748(1), 96–102 (2001)

3. Giorgino, T.: Computing and visualizing dynamic time warping alignments in r: the dtw package. J. Stat. Softw. 31, 1–24 (2009)

4. Guo, S., Lin, Y., Wan, H., Li, X., Cong, G.: Learning dynamics and heterogeneity of spatial-temporal graph data for traffic forecasting. IEEE TKDE 34(11), 5415–5428 (2021)

5. He, H., Ye, K., Xu, C.Z.: Multi-feature urban traffic prediction based on unconstrained graph attention network. In: 2021 IEEE BigData, pp. 1409–1417 (2021)

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