STFDSGCN: Spatio-Temporal Fusion Graph Neural Network Based on Dynamic Sparse Graph Convolution GRU for Traffic Flow Forecast

STFDSGCN: Spatio-Temporal Fusion Graph Neural Network Based on Dynamic Sparse Graph Convolution GRU for Traffic Flow Forecast

The characteristics of multivariate heterogeneity in traffic flow forecasting exhibit significant variation, heavily influenced by spatio-temporal dynamics and unforeseen events. To address this challenge, we propose a spatio-temporal fusion graph neural network based on dynamic sparse graph convolu...

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Bibliographic Details
Main Authors: Jiahao Chang, Jiali Yin, Yanrong Hao, Chengxin Gao
Format: Article
Language:English
Published: MDPI AG 2025-05-01
Series:Sensors
Subjects:
traffic flow forecasting
graph neural networks (GNN)
gated recurrent units (GRUs)
dynamic sparse graph convolution
spatio-temporal attention
Online Access:https://www.mdpi.com/1424-8220/25/11/3446
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