Short-term multi-step-ahead sector-based traffic flow prediction based on the attention-enhanced graph convolutional LSTM network (AGC-LSTM)
- Standort
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Deutsche Nationalbibliothek Frankfurt am Main
- Umfang
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1 Online-Ressource.
- Sprache
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Englisch
- Erschienen in
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Short-term multi-step-ahead sector-based traffic flow prediction based on the attention-enhanced graph convolutional LSTM network (AGC-LSTM) ; day:7 ; month:5 ; year:2024 ; pages:1-20
Neural computing & applications ; (7.5.2024), 1-20
- Klassifikation
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Wirtschaft
- Urheber
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Zhang, Ying
Xu, Shimin
Zhang, Linghui
Jiang, Weiwei
Alam, Sameer
Xue, Dabin
- Beteiligte Personen und Organisationen
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SpringerLink (Online service)
- DOI
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10.1007/s00521-024-09827-3
- URN
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urn:nbn:de:101:1-2407291126589.608616045242
- Rechteinformation
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Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Letzte Aktualisierung
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14.08.2025, 10:46 MESZ
Datenpartner
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Beteiligte
- Zhang, Ying
- Xu, Shimin
- Zhang, Linghui
- Jiang, Weiwei
- Alam, Sameer
- Xue, Dabin
- SpringerLink (Online service)
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