AST3DRNet: Attention-Based Spatio-Temporal 3D Residual Neural Networks for Traffic Congestion Prediction

AST3DRNet: Attention-Based Spatio-Temporal 3D Residual Neural Networks for Traffic Congestion Prediction

Traffic congestion prediction has become an indispensable component of an intelligent transport system. However, one limitation of the existing methods is that they treat the effects of spatio-temporal correlations on traffic prediction as invariable during modeling spatio-temporal features, which r...

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Bibliographic Details
Main Authors: Lecheng Li, Fei Dai, Bi Huang, Shuai Wang, Wanchun Dou, Xiaodong Fu
Format: Article
Language:English
Published: MDPI AG 2024-02-01
Series:Sensors
Subjects:
traffic congestion prediction
3D convolution
3D residual unit
self-attention mechanism
spatio-temporal attention
Online Access:https://www.mdpi.com/1424-8220/24/4/1261

Internet

https://www.mdpi.com/1424-8220/24/4/1261

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