Spatiotemporal-decoupled interactive learning for traffic flow prediction

Abstract Accurate traffic flow prediction is a core capability of intelligent transportation systems, supporting trip planning, network dispatch, and management decisions. Most existing approaches overlook interactive learning of spatiotemporal dependencies and struggle to accommodate pattern divers...

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Détails bibliographiques

Auteurs principaux:	Linlong Chen, Qingfang Wu
Format:	Artigo
Langue:	Inglês
Publié:	Nature Portfolio 2026-02-01
Collection:	Scientific Reports
Sujets:	Traffic flow prediction Graph neural networks Spatiotemporal decoupling Interactive learning Spatiotemporal heterogeneity
Accès en ligne:	https://doi.org/10.1038/s41598-026-38244-w
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