ClusterST: Clustering Spatial–Temporal Network for Traffic Forecasting-Reference-Cited by-同舟云学术

ClusterST: Clustering Spatial–Temporal Network for Traffic Forecasting

Author:

Luo Guiyang¹^ORCID,Zhang Hui²,Yuan Quan¹^ORCID,Li Jinglin¹^ORCID,Wang Fei-Yue³^ORCID

Affiliation:

1. State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, China

2. School of Computer and Information Technology, Beijing Jiaotong University, Beijing, China

3. State Key Laboratory for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China

Funder

National Natural Science Foundation of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computer Science Applications,Mechanical Engineering,Automotive Engineering

Link

Reference60 articles.

1. Learning dynamic and hierarchical traffic spatiotemporal features with transformer;yan;IEEE Trans Intell Transp Syst,2021

2. PairNorm: Tackling oversmoothing in GNNs;zhao;arXiv 1909 12223,2019

5. Inductive representation learning on large graphs;hamilton;Proc Adv Neural Inf Process Syst,2017

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