Matches in SemOpenAlex for { <https://semopenalex.org/work/W3092339997> ?p ?o ?g. }
Showing items 1 to 95 of
95
with 100 items per page.
- W3092339997 endingPage "147" @default.
- W3092339997 startingPage "135" @default.
- W3092339997 abstract "Traffic accidents usually lead to severe human casualties and huge economic losses in real-world scenarios. Timely accurate prediction of traffic accidents has great potential to protect public safety and reduce economic losses. However, it is challenging to predict traffic accidents due to the complex causality of traffic accidents with multiple factors, including spatial correlations, temporal dynamic interactions and external influences in traffic-relevant heterogeneous data. To overcome the above issues, this paper proposes a novel Deep Spatio-Temporal Graph Convolutional Network, namely DSTGCN, to predict traffic accidents. The proposed model is composed of three components: the first component is the spatial learning layer which performs graph convolutional operations on spatial information to learn the correlations in space. The second component is the spatio-temporal learning layer which utilizes graph and standard convolutions to capture the dynamic variations in both spatial and temporal perspective. The third component is the embedding layer which aims to obtain meaningful and semantic representations of external information. To evaluate the proposed model, we collect large-scale real-world data, including accident records, citi-wide vehicle speeds, road networks, meteorological conditions, and Point-of-Interest distributions. Experimental results on real-world datasets demonstrate that DSTGCN outperforms both classical and state-of-the-art methods." @default.
- W3092339997 created "2020-10-15" @default.
- W3092339997 creator A5000608590 @default.
- W3092339997 creator A5005723679 @default.
- W3092339997 creator A5053487836 @default.
- W3092339997 creator A5056061158 @default.
- W3092339997 creator A5078691114 @default.
- W3092339997 creator A5081275566 @default.
- W3092339997 date "2021-01-01" @default.
- W3092339997 modified "2023-10-02" @default.
- W3092339997 title "Deep spatio-temporal graph convolutional network for traffic accident prediction" @default.
- W3092339997 cites W1969399478 @default.
- W3092339997 cites W2060758175 @default.
- W3092339997 cites W2067470563 @default.
- W3092339997 cites W2070463402 @default.
- W3092339997 cites W2080911646 @default.
- W3092339997 cites W2097513162 @default.
- W3092339997 cites W2101491865 @default.
- W3092339997 cites W2135046866 @default.
- W3092339997 cites W2139906443 @default.
- W3092339997 cites W2471223835 @default.
- W3092339997 cites W2792204625 @default.
- W3092339997 cites W2793870512 @default.
- W3092339997 cites W2807894308 @default.
- W3092339997 cites W2808862972 @default.
- W3092339997 cites W2809035759 @default.
- W3092339997 cites W2919115771 @default.
- W3092339997 cites W2966210862 @default.
- W3092339997 cites W309214422 @default.
- W3092339997 cites W3103720336 @default.
- W3092339997 cites W4210257598 @default.
- W3092339997 cites W4236137412 @default.
- W3092339997 cites W4239510810 @default.
- W3092339997 doi "https://doi.org/10.1016/j.neucom.2020.09.043" @default.
- W3092339997 hasPublicationYear "2021" @default.
- W3092339997 type Work @default.
- W3092339997 sameAs 3092339997 @default.
- W3092339997 citedByCount "61" @default.
- W3092339997 countsByYear W30923399972020 @default.
- W3092339997 countsByYear W30923399972021 @default.
- W3092339997 countsByYear W30923399972022 @default.
- W3092339997 countsByYear W30923399972023 @default.
- W3092339997 crossrefType "journal-article" @default.
- W3092339997 hasAuthorship W3092339997A5000608590 @default.
- W3092339997 hasAuthorship W3092339997A5005723679 @default.
- W3092339997 hasAuthorship W3092339997A5053487836 @default.
- W3092339997 hasAuthorship W3092339997A5056061158 @default.
- W3092339997 hasAuthorship W3092339997A5078691114 @default.
- W3092339997 hasAuthorship W3092339997A5081275566 @default.
- W3092339997 hasConcept C108583219 @default.
- W3092339997 hasConcept C121332964 @default.
- W3092339997 hasConcept C124101348 @default.
- W3092339997 hasConcept C132525143 @default.
- W3092339997 hasConcept C154945302 @default.
- W3092339997 hasConcept C159620131 @default.
- W3092339997 hasConcept C168167062 @default.
- W3092339997 hasConcept C205649164 @default.
- W3092339997 hasConcept C41008148 @default.
- W3092339997 hasConcept C41608201 @default.
- W3092339997 hasConcept C62649853 @default.
- W3092339997 hasConcept C80444323 @default.
- W3092339997 hasConcept C97355855 @default.
- W3092339997 hasConceptScore W3092339997C108583219 @default.
- W3092339997 hasConceptScore W3092339997C121332964 @default.
- W3092339997 hasConceptScore W3092339997C124101348 @default.
- W3092339997 hasConceptScore W3092339997C132525143 @default.
- W3092339997 hasConceptScore W3092339997C154945302 @default.
- W3092339997 hasConceptScore W3092339997C159620131 @default.
- W3092339997 hasConceptScore W3092339997C168167062 @default.
- W3092339997 hasConceptScore W3092339997C205649164 @default.
- W3092339997 hasConceptScore W3092339997C41008148 @default.
- W3092339997 hasConceptScore W3092339997C41608201 @default.
- W3092339997 hasConceptScore W3092339997C62649853 @default.
- W3092339997 hasConceptScore W3092339997C80444323 @default.
- W3092339997 hasConceptScore W3092339997C97355855 @default.
- W3092339997 hasLocation W30923399971 @default.
- W3092339997 hasOpenAccess W3092339997 @default.
- W3092339997 hasPrimaryLocation W30923399971 @default.
- W3092339997 hasRelatedWork W2126887587 @default.
- W3092339997 hasRelatedWork W2379533788 @default.
- W3092339997 hasRelatedWork W2731899572 @default.
- W3092339997 hasRelatedWork W2939353110 @default.
- W3092339997 hasRelatedWork W2941846814 @default.
- W3092339997 hasRelatedWork W2948658236 @default.
- W3092339997 hasRelatedWork W3009238340 @default.
- W3092339997 hasRelatedWork W3118091236 @default.
- W3092339997 hasRelatedWork W3215138031 @default.
- W3092339997 hasRelatedWork W4230611425 @default.
- W3092339997 hasVolume "423" @default.
- W3092339997 isParatext "false" @default.
- W3092339997 isRetracted "false" @default.
- W3092339997 magId "3092339997" @default.
- W3092339997 workType "article" @default.