Universal Transfer Framework for Urban Spatiotemporal Knowledge Based on Radial Basis Function-Reference-Cited by-同舟云学术

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Universal Transfer Framework for Urban Spatiotemporal Knowledge Based on Radial Basis Function

Published:2024-09 Issue:9 Volume:5 Page:4458-4469
ISSN:2691-4581
Container-title:IEEE Transactions on Artificial Intelligence
language:
Short-container-title:IEEE Trans. Artif. Intell.

Author:

Chiu Sheng-Min¹^ORCID,Liou Yow-Shin²^ORCID,Chen Yi-Chung³^ORCID,Lee Chiang²^ORCID,Shang Rong-Kang⁴^ORCID,Chang Tzu-Yin⁴^ORCID,Zimmermann Roger¹^ORCID

Affiliation:

1. School of Computing, National University of Singapore, Singapore

2. Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan, Taiwan

3. National Chung Hsing University, Taichung, Taiwan

4. National Science and Technology Center for Disaster Reduction, New Taipei City, Taiwan

Funder

Ministry of Science and Technology, Taiwan

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/9078688/10673734/10481516.pdf?arnumber=10481516

Reference30 articles.

1. Deep Spatio-Temporal Residual Networks for Citywide Crowd Flows Prediction

2. Personalized location recommendation using mobile phone usage information

3. Fine-Grained Urban Flow Prediction

4. Cross-City Transfer Learning for Deep Spatio-Temporal Prediction

5. Learning from Multiple Cities: A Meta-Learning Approach for Spatial-Temporal Prediction

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1. Deep Learning-Based Approach for Optimizing Urban Commercial Space Expansion Using Artificial Neural Networks;Applied Sciences;2024-04-30

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