Cross-Attention Fusion Based Spatial-Temporal Multi-Graph Convolutional Network for Traffic Flow Prediction

Cross-Attention Fusion Based Spatial-Temporal Multi-Graph Convolutional Network for Traffic Flow Prediction

Accurate traffic flow prediction is essential to building a smart transportation city. Existing research mainly uses a given single-graph structure as a model, only considers local and static spatial dependencies, and ignores the impact of dynamic spatio-temporal data diversity. To fully capture the...

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Bibliographic Details
Main Authors: Kun Yu, Xizhong Qin, Zhenhong Jia, Yan Du, Mengmeng Lin
Format: Article
Language:English
Published: MDPI AG 2021-12-01
Series:Sensors
Subjects:
traffic flow prediction
data diversity
cross-attention
spatio-temporal multi-graph
Online Access:https://www.mdpi.com/1424-8220/21/24/8468

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https://www.mdpi.com/1424-8220/21/24/8468

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