A spatiotemporal graph generative adversarial networks for short-term passenger flow prediction in urban rail transit systems-Reference-Cited by-同舟云学术

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A spatiotemporal graph generative adversarial networks for short-term passenger flow prediction in urban rail transit systems

Published:2023-05-09 Issue:6 Volume:52 Page:694-721
ISSN:0308-1079
Container-title:International Journal of General Systems
language:en
Short-container-title:International Journal of General Systems

Author:

Zhang Jinlei¹,Li Hua¹,Zhang Shuxin¹,Yang Lixing¹²,Jin Guangyin³,Qi Jianguo¹

Affiliation:

1. School of Systems Science, Beijing Jiaotong University, Beijing, China

2. School of Traffic and Transportation, Lanzhou Jiaotong University, Lanzhou, China

3. College of System Engineering, National University of Defense Technology, Changsha, China

Funder

National Natural Science Foundation of China

China Postdoctoral Science Foundation

Publisher

Informa UK Limited

Subject

Computer Science Applications,Modeling and Simulation,Information Systems,Theoretical Computer Science,Control and Systems Engineering

Link

https://www.tandfonline.com/doi/pdf/10.1080/03081079.2023.2203922

Reference61 articles.

1. CVAE-GAN: Fine-Grained Image Generation through Asymmetric Training

2. Incorporating travel behavior regularity into passenger flow forecasting

3. GAN-BERT: Generative Adversarial Learning for Robust Text Classification with a Bunch of Labeled Examples

4. Traffic Graph Convolutional Recurrent Neural Network: A Deep Learning Framework for Network-Scale Traffic Learning and Forecasting

5. Prediction of City-Scale Dynamic Taxi Origin-Destination Flows Using a Hybrid Deep Neural Network Combined With Travel Time

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