Matches in SemOpenAlex for { <https://semopenalex.org/work/W3103885348> ?p ?o ?g. }
Showing items 1 to 85 of
85
with 100 items per page.
- W3103885348 abstract "Accurate and timely provided network traffic data is important for a large number of applications in traffic management, urban planningm and guidance. Traffic forecasting remains a very challenging problem since the traffic flows usually show complex non-linear traffic patterns and have spatial dependencies on the road networks. Existing methods and algorithms usually consider spatial and temporal correlations in traffic data separately. In this paper, we investigate deep convolutional neural networks on graphs to solve short-term traffic forecasting problems. The considered graph convolutional networks are able to efficiently capture spatio-temporal correlations in traffic data. Experimental results show that the considered model outperforms the baseline methods on the transportation network of the Samara city, Russia." @default.
- W3103885348 created "2020-11-23" @default.
- W3103885348 creator A5022469144 @default.
- W3103885348 creator A5073537004 @default.
- W3103885348 date "2020-05-26" @default.
- W3103885348 modified "2023-10-18" @default.
- W3103885348 title "Spatio-Temporal Graph Convolutional Networks for Short-Term Traffic Forecasting" @default.
- W3103885348 cites W2036785686 @default.
- W3103885348 cites W2040297119 @default.
- W3103885348 cites W2131767615 @default.
- W3103885348 cites W2158787690 @default.
- W3103885348 cites W2163089819 @default.
- W3103885348 cites W2217607432 @default.
- W3103885348 cites W2519887557 @default.
- W3103885348 cites W2579495707 @default.
- W3103885348 cites W2613331518 @default.
- W3103885348 cites W2624190409 @default.
- W3103885348 cites W2903871660 @default.
- W3103885348 cites W2905702505 @default.
- W3103885348 cites W2907162738 @default.
- W3103885348 cites W2954757391 @default.
- W3103885348 cites W2963124587 @default.
- W3103885348 cites W2964311892 @default.
- W3103885348 cites W2964321699 @default.
- W3103885348 cites W2985331920 @default.
- W3103885348 cites W2996451395 @default.
- W3103885348 cites W3103720336 @default.
- W3103885348 doi "https://doi.org/10.1109/itnt49337.2020.9253169" @default.
- W3103885348 hasPublicationYear "2020" @default.
- W3103885348 type Work @default.
- W3103885348 sameAs 3103885348 @default.
- W3103885348 citedByCount "1" @default.
- W3103885348 countsByYear W31038853482022 @default.
- W3103885348 crossrefType "proceedings-article" @default.
- W3103885348 hasAuthorship W3103885348A5022469144 @default.
- W3103885348 hasAuthorship W3103885348A5073537004 @default.
- W3103885348 hasConcept C111368507 @default.
- W3103885348 hasConcept C121332964 @default.
- W3103885348 hasConcept C124101348 @default.
- W3103885348 hasConcept C12725497 @default.
- W3103885348 hasConcept C127313418 @default.
- W3103885348 hasConcept C132525143 @default.
- W3103885348 hasConcept C154945302 @default.
- W3103885348 hasConcept C176715033 @default.
- W3103885348 hasConcept C41008148 @default.
- W3103885348 hasConcept C61797465 @default.
- W3103885348 hasConcept C62520636 @default.
- W3103885348 hasConcept C67186912 @default.
- W3103885348 hasConcept C77088390 @default.
- W3103885348 hasConcept C79403827 @default.
- W3103885348 hasConcept C80444323 @default.
- W3103885348 hasConcept C81363708 @default.
- W3103885348 hasConceptScore W3103885348C111368507 @default.
- W3103885348 hasConceptScore W3103885348C121332964 @default.
- W3103885348 hasConceptScore W3103885348C124101348 @default.
- W3103885348 hasConceptScore W3103885348C12725497 @default.
- W3103885348 hasConceptScore W3103885348C127313418 @default.
- W3103885348 hasConceptScore W3103885348C132525143 @default.
- W3103885348 hasConceptScore W3103885348C154945302 @default.
- W3103885348 hasConceptScore W3103885348C176715033 @default.
- W3103885348 hasConceptScore W3103885348C41008148 @default.
- W3103885348 hasConceptScore W3103885348C61797465 @default.
- W3103885348 hasConceptScore W3103885348C62520636 @default.
- W3103885348 hasConceptScore W3103885348C67186912 @default.
- W3103885348 hasConceptScore W3103885348C77088390 @default.
- W3103885348 hasConceptScore W3103885348C79403827 @default.
- W3103885348 hasConceptScore W3103885348C80444323 @default.
- W3103885348 hasConceptScore W3103885348C81363708 @default.
- W3103885348 hasLocation W31038853481 @default.
- W3103885348 hasOpenAccess W3103885348 @default.
- W3103885348 hasPrimaryLocation W31038853481 @default.
- W3103885348 hasRelatedWork W11644230 @default.
- W3103885348 hasRelatedWork W12196170 @default.
- W3103885348 hasRelatedWork W12712126 @default.
- W3103885348 hasRelatedWork W13426426 @default.
- W3103885348 hasRelatedWork W5668360 @default.
- W3103885348 hasRelatedWork W6946022 @default.
- W3103885348 hasRelatedWork W7842670 @default.
- W3103885348 hasRelatedWork W8190784 @default.
- W3103885348 hasRelatedWork W9448574 @default.
- W3103885348 hasRelatedWork W9753090 @default.
- W3103885348 isParatext "false" @default.
- W3103885348 isRetracted "false" @default.
- W3103885348 magId "3103885348" @default.
- W3103885348 workType "article" @default.