TEA-GCN: Transformer-Enhanced Adaptive Graph Convolutional Network for Traffic Flow Forecasting

TEA-GCN: Transformer-Enhanced Adaptive Graph Convolutional Network for Traffic Flow Forecasting

Traffic flow forecasting is crucial for improving urban traffic management and reducing resource consumption. Accurate traffic conditions prediction requires capturing the complex spatial-temporal dependencies inherent in traffic data. Traditional spatial-temporal graph modeling methods often rely o...

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Bibliographic Details
Main Authors: Xiaxia He, Wenhui Zhang, Xiaoyu Li, Xiaodan Zhang
Format: Article
Language:English
Published: MDPI AG 2024-11-01
Series:Sensors
Subjects:
graph convolutional networks
traffic flow forecasting
adaptive graph learning
Online Access:https://www.mdpi.com/1424-8220/24/21/7086
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