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KST-GCN: A Knowledge-Driven Spatial-Temporal Graph Convolutional Network for Traffic Forecasting. (arXiv:2011.14992v2 [cs.LG] UPDATED)
Jan. 20, 2022, 2:11 a.m. | Jiawei Zhu, Xin Han, Hanhan Deng, Chao Tao, Ling Zhao, Pu Wang, Lin Tao, Haifeng Li
cs.LG updates on arXiv.org arxiv.org
While considering the spatial and temporal features of traffic, capturing the
impacts of various external factors on travel is an essential step towards
achieving accurate traffic forecasting. However, existing studies seldom
consider external factors or neglect the effect of the complex correlations
among external factors on traffic. Intuitively, knowledge graphs can naturally
describe these correlations. Since knowledge graphs and traffic networks are
essentially heterogeneous networks, it is challenging to integrate the
information in both networks. On this background, this study …
More from arxiv.org / cs.LG updates on arXiv.org
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