Multi-view Bayesian spatio-temporal graph neural networks for reliable traffic flow prediction-Reference-Cited by-同舟云学术

Multi-view Bayesian spatio-temporal graph neural networks for reliable traffic flow prediction

Published:2022-10-20 Issue: Volume: Page:
ISSN:1868-8071
Container-title:International Journal of Machine Learning and Cybernetics
language:en
Short-container-title:Int. J. Mach. Learn. & Cyber.

Author:

Xia Jiangnan,Wang Senzhang^ORCID,Wang Xiang,Xia Min,Xie Kun,Cao Jiannong

Funder

Natural Science Foundation of China

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Computer Vision and Pattern Recognition,Software

Link

https://link.springer.com/content/pdf/10.1007/s13042-022-01689-2.pdf

Reference42 articles.

1. Wang S, Cao J, Yu P (2020) Deep learning for spatio-temporal data mining: a survey. IEEE Trans Knowl Data Eng 2:2

2. Pal C, Hirayama S, Narahari S, Jeyabharath M, Prakash G, Kulothungan V (2018) An insight of world health organization (who) accident database by cluster analysis with self-organizing map (som). Traffic Inj Prev 19(sup1):S15–S20

3. Tedjopurnomo DA, Bao Z, Zheng B, Choudhury F, Qin A (2020) A survey on modern deep neural network for traffic prediction: trends, methods and challenges. IEEE Trans Knowl Data Eng 2:2

4. Wang J, Chen Q, Gong H (2020) Stmag: a spatial-temporal mixed attention graph-based convolution model for multi-data flow safety prediction. Inf Sci 525:16–36

5. Chandra SR, Al-Deek H (2009) Predictions of freeway traffic speeds and volumes using vector autoregressive models. J Intell Transp Syst 13(2):53–72

Cited by 9 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. Koopman theory meets graph convolutional network: Learning the complex dynamics of non-stationary highway traffic flow for spatiotemporal prediction;Chaos, Solitons & Fractals;2024-10

2. Measuring the Confidence of Single-Point Traffic Forecasting Models: Techniques, Experimental Comparison, and Guidelines Toward Their Actionability;IEEE Transactions on Intelligent Transportation Systems;2024-09

3. Multi-graph aggregated graph neural network for heterogeneous graph representation learning;International Journal of Machine Learning and Cybernetics;2024-08-05

4. A Rotating Machinery Fault Diagnosis Method Based on Dynamic Graph Convolution Network and Hard Threshold Denoising;Sensors;2024-07-27

5. A heterogeneous traffic spatio-temporal graph convolution model for traffic prediction;Physica A: Statistical Mechanics and its Applications;2024-05