GST-GCN: A Geographic-Semantic-Temporal Graph Convolutional Network for Context-aware Traffic Flow Prediction on Graph Sequences-Reference-Cited by-同舟云学术

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GST-GCN: A Geographic-Semantic-Temporal Graph Convolutional Network for Context-aware Traffic Flow Prediction on Graph Sequences

Published:2020-10-11 Issue: Volume: Page:
ISSN:
Container-title:2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC)
language:
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Author:

Chen Jing,Liao Shijie,Hou Jiaxin,Wang Kesu,Wen Junhao

Funder

Fundamental Research Funds for the Central Universities

Research and Development

Chongqing Municipal Education Commission

Natural Science Foundation of Chongqing

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9282733/9282811/09282828.pdf?arnumber=9282828

Cited by 11 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. Integrating Graph Neural Network and GPS Data for Hourly Travel Time Estimation in Bangkok Rama-4 Road Network;2023 7th International Conference on Information Technology (InCIT);2023-11-16

2. Incorporating multimodal context information into traffic speed forecasting through graph deep learning;International Journal of Geographical Information Science;2023-07-18

3. A network traffic prediction model based on reinforced staged feature interaction and fusion;Computer Networks;2023-05

4. Multi-Head Spatiotemporal Attention Graph Convolutional Network for Traffic Prediction;Sensors;2023-04-09

5. Geography-based Neural Networks for the Simulation of Air Pollution;IFAC-PapersOnLine;2023

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