Highway traffic flow prediction model with multi-component spatial–temporal graph convolution networks
- Standort
-
Deutsche Nationalbibliothek Frankfurt am Main
- ISSN
-
2045-2322
- Umfang
-
Online-Ressource
- Sprache
-
Englisch
- Anmerkungen
-
online resource.
- Erschienen in
-
Highway traffic flow prediction model with multi-component spatial–temporal graph convolution networks ; volume:12 ; number:1 ; day:14 ; month:12 ; year:2022 ; pages:1-11 ; date:12.2022
Scientific reports ; 12, Heft 1 (14.12.2022), 1-11, 12.2022
- Urheber
-
Ning, Tao
Han, Yumeng
Wang, Jiayu
- Beteiligte Personen und Organisationen
-
SpringerLink (Online service)
- DOI
-
10.1038/s41598-022-18027-9
- URN
-
urn:nbn:de:101:1-2023022321094026090900
- Rechteinformation
-
Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Letzte Aktualisierung
-
14.08.2025, 10:51 MESZ
Datenpartner
Dieses Objekt wird bereitgestellt von:
Deutsche Nationalbibliothek. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Deutsche Nationalbibliothek. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Beteiligte
- Ning, Tao
- Han, Yumeng
- Wang, Jiayu
- SpringerLink (Online service)
Ähnliche Objekte (12)
Spatial-Temporal Phenomena in the Flows of Multi-Component
STDC-Net: A spatial-temporal deformable convolution network for conference video frame interpolation
A task-unified network with transformer and spatial–temporal convolution for left ventricular quantification
Model-enhanced spatial-temporal attention networks for traffic density prediction
Generalized spatial–temporal regression graph convolutional transformer for traffic forecasting
Traffic flow prediction based on depthwise separable convolution fusion network
A multi-feature spatial–temporal fusion network for traffic flow prediction
GIS-based analysis of spatial–temporal correlations of urban traffic accidents
Spatial linear transformer and temporal convolution network for traffic flow prediction
Traffic management and safety at highway work zones : report
Potential for Enhancing Traffic Safety on Highways of Russia
Stochastic and self-similar nature of highway traffic data
Spatial-Temporal Phenomena in the Flows of Multi-Component
STDC-Net: A spatial-temporal deformable convolution network for conference video frame interpolation
A task-unified network with transformer and spatial–temporal convolution for left ventricular quantification
Model-enhanced spatial-temporal attention networks for traffic density prediction
Generalized spatial–temporal regression graph convolutional transformer for traffic forecasting
Traffic flow prediction based on depthwise separable convolution fusion network
A multi-feature spatial–temporal fusion network for traffic flow prediction
GIS-based analysis of spatial–temporal correlations of urban traffic accidents
Spatial linear transformer and temporal convolution network for traffic flow prediction
Traffic management and safety at highway work zones : report
Potential for Enhancing Traffic Safety on Highways of Russia
Stochastic and self-similar nature of highway traffic data
Spatial-Temporal Phenomena in the Flows of Multi-Component
STDC-Net: A spatial-temporal deformable convolution network for conference video frame interpolation
A task-unified network with transformer and spatial–temporal convolution for left ventricular quantification
Model-enhanced spatial-temporal attention networks for traffic density prediction
Generalized spatial–temporal regression graph convolutional transformer for traffic forecasting
Traffic flow prediction based on depthwise separable convolution fusion network
A multi-feature spatial–temporal fusion network for traffic flow prediction
GIS-based analysis of spatial–temporal correlations of urban traffic accidents
Spatial linear transformer and temporal convolution network for traffic flow prediction
Traffic management and safety at highway work zones : report
Potential for Enhancing Traffic Safety on Highways of Russia