Urban rail transit passenger flow forecast based on LSTM with enhanced long-term features (Englisch)
Freier Zugriff
- Neue Suche nach: Yang, Dan
- Neue Suche nach: Chen, Kairun
- Neue Suche nach: Yang, Mengning
- Neue Suche nach: Zhao, Xiaochao
- Neue Suche nach: Yang, Dan
- Neue Suche nach: Chen, Kairun
- Neue Suche nach: Yang, Mengning
- Neue Suche nach: Zhao, Xiaochao
In:
IET Intelligent Transport Systems
;
13
, 10
;
1475-1482
;
2019
- Aufsatz (Zeitschrift) / Elektronische Ressource
-
Titel:Urban rail transit passenger flow forecast based on LSTM with enhanced long-term features
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Beteiligte:Yang, Dan ( Autor:in ) / Chen, Kairun ( Autor:in ) / Yang, Mengning ( Autor:in ) / Zhao, Xiaochao ( Autor:in )
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Erschienen in:IET Intelligent Transport Systems ; 13, 10 ; 1475-1482
-
Verlag:
- Neue Suche nach: The Institution of Engineering and Technology
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Erscheinungsdatum:04.07.2019
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Format / Umfang:8 pages
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ISSN:
-
DOI:
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Medientyp:Aufsatz (Zeitschrift)
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Format:Elektronische Ressource
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Sprache:Englisch
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Schlagwörter:learning (artificial intelligence) , traffic control department , short-term features , origin destination flow , neural nets , road traffic , time series , accurate forecast , urban rail transit passenger flow , forecasting theory , improved model enhanced long-term features , traffic control , ELF-LSTM , rail transit system , rail traffic , long-short-term memory , LSTM Neural Network models , passenger flow data , transportation , LSTM NN , long temporal dependency features , rail transit congestion , traffic sequence , long temporal dependence , traffic engineering computing , typical time series , recurrent neural nets , outbreak passenger flow , traffic congestion
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Datenquelle:
Metadata by IET is licensed under CC BY 3.0
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