Integrating knowledge representation into traffic prediction: a spatial–temporal graph neural networ...
Integrating knowledge representation into traffic prediction: a spatial–temporal graph neural network with adaptive fusion features
About this item
Full title
Author / Creator
Zhou, Yi , Liu, Yihan , Ning, Nianwen , Wang, Li , Zhang, Zixing , Gao, Xiaozhi and Lu, Ning
Publisher
Cham: Springer International Publishing
Journal title
Language
English
Formats
Publication information
Publisher
Cham: Springer International Publishing
Subjects
More information
Scope and Contents
Contents
Various external factors that interfere with traffic flow, such as weather conditions, traffic accidents, incidents, and Points of Interest (POIs), need to be considered in performing traffic forecasting tasks. However, the current research methods encounter difficulties in effectively incorporating these factors with traffic characteristics and ef...
Alternative Titles
Full title
Integrating knowledge representation into traffic prediction: a spatial–temporal graph neural network with adaptive fusion features
Authors, Artists and Contributors
Author / Creator
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Primary Identifiers
Record Identifier
TN_cdi_doaj_primary_oai_doaj_org_article_ea8e4fda50814d0aa82363da6630f77d
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_ea8e4fda50814d0aa82363da6630f77d
Other Identifiers
ISSN
2199-4536
E-ISSN
2198-6053
DOI
10.1007/s40747-023-01299-7
