Traffic Flow Prediction Based on Decoupled Dynamic Spatio-Temporal Convolutional Recurrent Network

This paper introduces a traffic flow prediction model using a Decoupled Dynamic Spatio-Temporal Convolutional Recurrent Network (DDSTCRN) to improve the exploration of complex spatio-temporal correlations in existing traffic flow prediction models. First, the data-decoupling module uses gating and r...

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Bibliografiska uppgifter

Huvudupphov:	WU Yongqing, JIANG Zhengyu
Materialtyp:	Artigo
Språk:	Inglês
Utgiven:	Editorial Office of Computer Engineering 2026-05-01
Serie:	Jisuanji gongcheng
Ämnen:	intelligent transportation|spatio-temporal feature|graph convolution network (gcn)|gated recurrent unit (gru)|decoupling
Länkar:	https://www.ecice06.com/fileup/1000-3428/PDF/20260512.pdf
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