Multi-factor embedding GNN-based traffic flow prediction considering intersection similarity

Existing studies on traffic flow prediction primarily rely on on-board devices to collect vehicle trajectory data, which can potentially infringe upon the privacy of users and limit the applicability of the method. Additionally, traffic flow prediction remains challenging due to the complex spatial...

Ausführliche Beschreibung

Bibliographische Detailangaben

Veröffentlicht in:	Neurocomputing (Amsterdam) Jg. 620; S. 129193
Hauptverfasser:	Zhong, Ruirui, Hu, Bingtao, Wang, Fei, Feng, Yixiong, Li, Zhiwu, Song, Xiuju, Wang, Yong, Lou, Shanhe, Tan, Jianrong
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier B.V 01.03.2025
Schlagworte:	Graph neural network Multi-factor Representation learning Spatio-temporal modeling Traffic flow prediction Representation learning Traffic flow prediction Multi-factor Graph neural network Spatio-temporal modeling
ISSN:	0925-2312
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!