Matches in SemOpenAlex for { <https://semopenalex.org/work/W4385978275> ?p ?o ?g. }
Showing items 1 to 85 of
85
with 100 items per page.
- W4385978275 endingPage "346" @default.
- W4385978275 startingPage "346" @default.
- W4385978275 abstract "Traffic prediction plays a significant part in creating intelligent cities such as traffic management, urban computing, and public safety. Nevertheless, the complex spatio-temporal linkages and dynamically shifting patterns make it somewhat challenging. Existing mainstream traffic prediction approaches heavily rely on graph convolutional networks and sequence prediction methods to extract complicated spatio-temporal patterns statically. However, they neglect to account for dynamic underlying correlations and thus fail to produce satisfactory prediction results. Therefore, we propose a novel Self-Adaptive Spatio-Temporal Graph Convolutional Network (SASTGCN) for traffic prediction. A self-adaptive calibrator, a spatio-temporal feature extractor, and a predictor comprise the bulk of the framework. To extract the distribution bias of the input in the self-adaptive calibrator, we employ a self-supervisor made of an encoder–decoder structure. The concatenation of the bias and the original characteristics are provided as input to the spatio-temporal feature extractor, which leverages a transformer and graph convolution structures to learn the spatio-temporal pattern, and then applies a predictor to produce the final prediction. Extensive trials on two public traffic prediction datasets (METR-LA and PEMS-BAY) demonstrate that SASTGCN surpasses the most recent techniques in several metrics." @default.
- W4385978275 created "2023-08-19" @default.
- W4385978275 creator A5028107330 @default.
- W4385978275 creator A5028226283 @default.
- W4385978275 creator A5037669752 @default.
- W4385978275 creator A5067740936 @default.
- W4385978275 creator A5077474570 @default.
- W4385978275 creator A5083551128 @default.
- W4385978275 date "2023-08-18" @default.
- W4385978275 modified "2023-10-14" @default.
- W4385978275 title "SASTGCN: A Self-Adaptive Spatio-Temporal Graph Convolutional Network for Traffic Prediction" @default.
- W4385978275 cites W2004353783 @default.
- W4385978275 cites W2038622450 @default.
- W4385978275 cites W2064675550 @default.
- W4385978275 cites W2069929199 @default.
- W4385978275 cites W2125817951 @default.
- W4385978275 cites W2133747588 @default.
- W4385978275 cites W2144475703 @default.
- W4385978275 cites W2147800946 @default.
- W4385978275 cites W2157331557 @default.
- W4385978275 cites W2165991108 @default.
- W4385978275 cites W2297059404 @default.
- W4385978275 cites W2528639018 @default.
- W4385978275 cites W2575125657 @default.
- W4385978275 cites W2788134583 @default.
- W4385978275 cites W2811044697 @default.
- W4385978275 cites W2904827256 @default.
- W4385978275 cites W2965341826 @default.
- W4385978275 cites W2995310170 @default.
- W4385978275 cites W2998436408 @default.
- W4385978275 cites W3034749137 @default.
- W4385978275 cites W3080253043 @default.
- W4385978275 cites W3174022889 @default.
- W4385978275 cites W3207996996 @default.
- W4385978275 cites W3212989947 @default.
- W4385978275 cites W4213090353 @default.
- W4385978275 cites W4293168685 @default.
- W4385978275 cites W4315647796 @default.
- W4385978275 doi "https://doi.org/10.3390/ijgi12080346" @default.
- W4385978275 hasPublicationYear "2023" @default.
- W4385978275 type Work @default.
- W4385978275 citedByCount "0" @default.
- W4385978275 crossrefType "journal-article" @default.
- W4385978275 hasAuthorship W4385978275A5028107330 @default.
- W4385978275 hasAuthorship W4385978275A5028226283 @default.
- W4385978275 hasAuthorship W4385978275A5037669752 @default.
- W4385978275 hasAuthorship W4385978275A5067740936 @default.
- W4385978275 hasAuthorship W4385978275A5077474570 @default.
- W4385978275 hasAuthorship W4385978275A5083551128 @default.
- W4385978275 hasBestOaLocation W43859782751 @default.
- W4385978275 hasConcept C119857082 @default.
- W4385978275 hasConcept C124101348 @default.
- W4385978275 hasConcept C132525143 @default.
- W4385978275 hasConcept C154945302 @default.
- W4385978275 hasConcept C41008148 @default.
- W4385978275 hasConcept C70437156 @default.
- W4385978275 hasConcept C80444323 @default.
- W4385978275 hasConceptScore W4385978275C119857082 @default.
- W4385978275 hasConceptScore W4385978275C124101348 @default.
- W4385978275 hasConceptScore W4385978275C132525143 @default.
- W4385978275 hasConceptScore W4385978275C154945302 @default.
- W4385978275 hasConceptScore W4385978275C41008148 @default.
- W4385978275 hasConceptScore W4385978275C70437156 @default.
- W4385978275 hasConceptScore W4385978275C80444323 @default.
- W4385978275 hasFunder F4320321001 @default.
- W4385978275 hasIssue "8" @default.
- W4385978275 hasLocation W43859782751 @default.
- W4385978275 hasOpenAccess W4385978275 @default.
- W4385978275 hasPrimaryLocation W43859782751 @default.
- W4385978275 hasRelatedWork W147410782 @default.
- W4385978275 hasRelatedWork W2152352598 @default.
- W4385978275 hasRelatedWork W2626256601 @default.
- W4385978275 hasRelatedWork W2900413183 @default.
- W4385978275 hasRelatedWork W2953234277 @default.
- W4385978275 hasRelatedWork W3015684221 @default.
- W4385978275 hasRelatedWork W3022252430 @default.
- W4385978275 hasRelatedWork W3103989898 @default.
- W4385978275 hasRelatedWork W4287804464 @default.
- W4385978275 hasRelatedWork W4287816705 @default.
- W4385978275 hasVolume "12" @default.
- W4385978275 isParatext "false" @default.
- W4385978275 isRetracted "false" @default.
- W4385978275 workType "article" @default.