DTM-GCN: A traffic flow prediction model based on dynamic graph convolutional network-Reference-Cited by-同舟云学术

DTM-GCN: A traffic flow prediction model based on dynamic graph convolutional network

Published:2024-02-15 Issue: Volume: Page:
ISSN:1573-7721
Container-title:Multimedia Tools and Applications
language:en
Short-container-title:Multimed Tools Appl

Author:

Wang Xinqiang,Shang Yihui,Li Guoyan^ORCID

Funder

National Natural Science Foundation of China

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s11042-024-18348-z.pdf

Reference29 articles.

1. Suhas S, Kalyan VV, Katti M et al (2017) A comprehensive review on traffic prediction for intelligent transport system. In: 2017 international conference on recent advances in electronics and communication technology (ICRAECT). IEEE, pp 138–143

2. Hao X, Jiayong L, Junfeng W (2021) Network traffic prediction based on neural networks and autoregressive models. Computer Applications 41(S1):180–184

3. Wei C, Yang H, Wang S et al (2018) Draft genome sequence of Camellia sinensis var. sinensis provides insights into the evolution of the tea genome and tea quality. Proc Natl Acad Sci 115(18):E4151–E4158

4. Hamed MM, Al-Masaeid HR, Said ZMB (1995) Short-term prediction of traffic volume in urban arterials. J Transp Eng 121(3):249–254

5. Uddin S, Haque I, Lu H et al (2022) Comparative performance analysis of K-nearest neighbour (KNN) algorithm and its different variants for disease prediction. Sci Rep 12(1):6256

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