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3D Temporal Graph Convolutional Networks (3D-TGCN)

Spatio-temporal prediction plays an important role in many application areas especially in traffic domain. However, due to complicated spatio-temporal dependency and high non-linear dynam-ics in road networks, traffic prediction task is still challenging.

Data and Resources

Cite this as

Bing Yu, Mengzhang Li, Jiyong Zhang, Zhanxing Zhu (2024). Dataset: 3D Temporal Graph Convolutional Networks (3D-TGCN). https://doi.org/10.57702/shj44ykh

DOI retrieved: December 3, 2024

Additional Info

Field Value
Created December 3, 2024
Last update December 3, 2024
Defined In https://doi.org/10.48550/arXiv.1903.00919
Author Bing Yu
More Authors
Mengzhang Li
Jiyong Zhang
Zhanxing Zhu
Homepage https://arxiv.org/abs/1906.09541