Dynamic Causal Graph Convolutional Network for Traffic Prediction-Reference-Cited by-同舟云学术

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Dynamic Causal Graph Convolutional Network for Traffic Prediction

Published:2023-08-26 Issue: Volume: Page:
ISSN:
Container-title:2023 IEEE 19th International Conference on Automation Science and Engineering (CASE)
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Author:

Lin Junpeng¹,Li Ziyue²,Li Zhishuai³,Bai Lei⁴,Zhao Rui⁵,Zhang Chen¹

Affiliation:

1. Tsinghua University,Department of Industrial Engineering,Beijing,China

2. University of Cologne,Cologne,NRW,Germany,50923

3. SenseTime Research,Shanghai,China

4. The Shanghai AI Laboratory,Shanghai,China

5. Institute of Shanghai Jiao Tong University,SenseTime Research and Qing Yuan Research,Shanghai,China

Funder

NSFC

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10260321/10260291/10260564.pdf?arnumber=10260564

Reference42 articles.

1. Jointly Contrastive Representation Learning on Road Network and Trajectory

2. Detecting and quantifying causal associations in large nonlinear time series datasets

3. Tensor Completion for Weakly-Dependent Data on Graph for Metro Passenger Flow Prediction

4. D’ya Like DAGs? A Survey on Structure Learning and Causal Discovery

5. Discussion of “A novel approach to the analysis of spatial and functional data over complex domains”

Cited by 3 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. From data to action in flood forecasting leveraging graph neural networks and digital twin visualization;Scientific Reports;2024-08-10

2. Gated Fusion Adaptive Graph Neural Network for Urban Road Traffic Flow Prediction;Neural Processing Letters;2024-02-06

3. Divide-and-Conquer Strategy for Large-Scale Dynamic Bayesian Network Structure Learning;IFIP Advances in Information and Communication Technology;2024

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