ST-DAGCN: A spatiotemporal dual adaptive graph convolutional network model for traffic prediction-Reference-Cited by-同舟云学术

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ST-DAGCN: A spatiotemporal dual adaptive graph convolutional network model for traffic prediction

Published:2024-10 Issue: Volume:601 Page:128175
ISSN:0925-2312
Container-title:Neurocomputing
language:en
Short-container-title:Neurocomputing

Author:

Liu Yutian^ORCID,Feng Tao^ORCID,Rasouli Soora,Wong Melvin

Publisher

Elsevier BV

Reference60 articles.

1. Dynamic prediction of traffic volume through Kalman filtering theory;Okutani;Transp. Res. B,1984

2. Online-SVR for short-term traffic flow prediction under typical and atypical traffic conditions;Castro-Neto;Expert Syst. Appl.,2009

3. Short-term traffic flow prediction using seasonal ARIMA model with limited input data;Kumar;Eur. Transp. Res. Rev.,2015

4. Deep learning on traffic prediction: Methods, analysis, and future directions;Yin;IEEE Trans. Intell. Transp. Syst.,2021

5. Short-term traffic prediction with deep neural networks: A survey;Lee;IEEE Access,2021

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