Traffic Data Imputation Using Deep Convolutional Neural Networks-Reference-Cited by-同舟云学术

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Traffic Data Imputation Using Deep Convolutional Neural Networks

Published:2020 Issue: Volume:8 Page:104740-104752
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Benkraouda Ouafa,Thodi Bilal Thonnam,Yeo Hwasoo,Menendez Monica^ORCID,Jabari Saif Eddin^ORCID

Funder

New York University Abu Dhabi (NYUAD) Center for Interacting Urban Networks

Tamkeen, through the New York University Abu Dhabi (NYUAD) Research Institute Award

Swiss Re Institute through the Quantum Cities™ Initiative

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science

Link

http://xplorestaging.ieee.org/ielx7/6287639/8948470/09107074.pdf?arnumber=9107074

Cited by 28 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. Spatial-temporal multi-factor fusion graph neural network for traffic prediction;Applied Intelligence;2024-07-16

2. Do traffic flow states follow Markov properties? A high-order spatiotemporal traffic state reconstruction approach for traffic prediction and imputation;Chaos, Solitons & Fractals;2024-06

3. Hierarchical spatio-temporal graph convolutional neural networks for traffic data imputation;Information Fusion;2024-06

4. An effective variational auto-encoder-based model for traffic flow imputation;Neural Computing and Applications;2023-11-22

5. A multi-directional recurrent graph convolutional network model for reconstructing traffic spatiotemporal diagram;Transportation Letters;2023-04-03

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