KST-GCN: A Knowledge-Driven Spatial-Temporal Graph Convolutional Network for Traffic Forecasting-Reference-Cited by-同舟云学术

KST-GCN: A Knowledge-Driven Spatial-Temporal Graph Convolutional Network for Traffic Forecasting

Author:

Zhu Jiawei¹^ORCID,Han Xing¹,Deng Hanhan²,Tao Chao¹^ORCID,Zhao Ling¹,Wang Pu³,Lin Tao⁴,Li Haifeng¹^ORCID

Affiliation:

1. School of Geosciences and Info-Physics, Central South University, Changsha, China

2. Hisense TransTech Company Ltd., Qingdao, China

3. School of Traffic and Transportation Engineering, Central South University, Changsha, China

4. College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, China

Funder

National Natural Science Foundation of China

Fundamental Research Funds for the Central Universities of Central South University

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computer Science Applications,Mechanical Engineering,Automotive Engineering

Link

Reference38 articles.

1. AST-GCN: Attribute-augmented spatiotemporal graph convolutional network for traffic forecasting;zhu;arXiv 2011 11004,2020

5. Hierarchical attentive knowledge graph embedding for personalized recommendation;sha;arXiv 1910 08288,2019

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