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TF-MVGNN: an accurate traffic forecasting framework based on spatial–temporal graph neural network through exploiting multiple-view graph construction and learning.

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Title: TF-MVGNN: an accurate traffic forecasting framework based on spatial–temporal graph neural network through exploiting multiple-view graph construction and learning.
Authors: Cheng, Haoyuan1, Wang, Yufeng1, wfwang@njupt.edu.cn, Ma, Jianhua2, jianhua@hosei.ac.jp, Jin, Qun3, jin@waseda.jp
Source: Neural Computing & Applications; Jul2025, Vol. 37 Issue 20, p14657-14671, 15p
Database: Applied Science & Technology Source
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Header DbId: aci
DbLabel: Applied Science & Technology Source
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PubType: Academic Journal
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  Data: TF-MVGNN: an accurate traffic forecasting framework based on spatial–temporal graph neural network through exploiting multiple-view graph construction and learning.
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RecordInfo BibRecord:
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      – Type: doi
        Value: 10.1007/s00521-024-10508-4
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      – Code: eng
        Text: English
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      – TitleFull: TF-MVGNN: an accurate traffic forecasting framework based on spatial–temporal graph neural network through exploiting multiple-view graph construction and learning.
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            NameFull: Cheng, Haoyuan
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            NameFull: Wang, Yufeng
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            NameFull: Ma, Jianhua
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              Text: Jul2025
              Type: published
              Y: 2025
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              Value: 37
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              Value: 20
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