Spatio-Temporal Joint Graph Convolutional Networks for Traffic Forecasting-Reference-Cited by-同舟云学术

Spatio-Temporal Joint Graph Convolutional Networks for Traffic Forecasting

Published:2024-01 Issue:1 Volume:36 Page:372-385
ISSN:1041-4347
Container-title:IEEE Transactions on Knowledge and Data Engineering
language:
Short-container-title:IEEE Trans. Knowl. Data Eng.

Author:

Zheng Chuanpan¹^ORCID,Fan Xiaoliang¹^ORCID,Pan Shirui²^ORCID,Jin Haibing¹^ORCID,Peng Zhaopeng¹^ORCID,Wu Zonghan³^ORCID,Wang Cheng¹^ORCID,Yu Philip S.⁴^ORCID

Affiliation:

1. Fujian Key Laboratory of Sensing and Computing for Smart Cities, School of Informatics, Computer Science and Technology Department, and Key Laboratory of Multimedia Trusted Perception and Efficient Computing, Ministry of Education of China, Xiamen University, Xiamen, China

2. School of Information and Communication Technology, Griffith University, Nathan, QLD, Australia

3. Centre for Artificial Intelligence, FEIT, University of Technology Sydney, Ultimo, NSW, Australia

4. Department of Computer Science, University of Illinois at Chicago, Chicago, IL, USA

Funder

National Natural Science Foundation of China

Fundamental Research Funds for the Central Universities

FuXiaQuan National Independent Innovation Demonstration Zone Collaborative Innovation Platform

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computational Theory and Mathematics,Computer Science Applications,Information Systems

Link

http://xplorestaging.ieee.org/ielx7/69/10348031/10151673.pdf?arnumber=10151673

Reference59 articles.

1. Deep Learning for Spatio-Temporal Data Mining: A Survey

2. A Survey on Modern Deep Neural Network for Traffic Prediction: Trends, Methods and Challenges

3. STPC-Net: Learn Massive Geo-Sensory Data as Spatio-Temporal Point Clouds

4. Diffusion convolutional recurrent neural network: Data-driven traffic forecasting;Li

5. A Neural Attention Model for Urban Air Quality Inference: Learning the Weights of Monitoring Stations

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