Graph learning-based spatial-temporal graph convolutional neural networks for traffic forecasting-Reference-Cited by-同舟云学术

Graph learning-based spatial-temporal graph convolutional neural networks for traffic forecasting

Published:2021-11-30 Issue: Volume: Page:1-20
ISSN:0954-0091
Container-title:Connection Science
language:en
Short-container-title:Connection Science

Author:

Hu Na¹,Zhang Dafang¹,Xie Kun¹,Liang Wei²,Hsieh Meng-Yen³

Affiliation:

1. College of Computer Science and Electronic Engineering, Hunan University, Changsha, People's Republic of China

2. School of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan, People's Republic of China

3. The Department of Computer Science and Information Engineering, Providence University, Taiwan, People's Republic of China

Funder

National Natural Science Foundation of China

Publisher

Informa UK Limited

Subject

Artificial Intelligence,Human-Computer Interaction,Software

Link

https://www.tandfonline.com/doi/pdf/10.1080/09540091.2021.2006607

Reference52 articles.

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2. Bahdanau, D., Cho, K. & Bengio, Y. (2014). Neural machine translation by jointly learning to align and translate. In 3rd International conference on learning representations, ICLR. OpenReview.net.

3. Forecasting holiday daily tourist flow based on seasonal support vector regression with adaptive genetic algorithm

4. Cho, K., Merrienboer, B. V., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H. & Bengio, Y. (2014). Learning phrase representations using RNN encoder-decoder for statistical machine translation. In Proceedings of the 2014 conference on empirical methods in natural language processing (pp. 1724–1734). ACL.

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