A fusion model of temporal graph attention network and machine learning for inferring commuting flow from human activity intensity dynamics

A fusion model of temporal graph attention network and machine learning for inferring commuting flow from human activity intensity dynamics

Accurately estimating commuting flow is essential for optimizing urban planning and traffic design. The latest graph neural network (GNN) model with the encoder-decoder-predictor components has several limitations. First, it ignores the temporal dependency of node features for node embedding. Second...

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Bibliographic Details
Main Authors:	Qingli Shi, Li Zhuo, Haiyan Tao, Junying Yang
Format:	Article
Language:	English
Published:	Elsevier 2024-02-01
Series:	International Journal of Applied Earth Observations and Geoinformation
Subjects:	Temporal convolution network Graph attention network Human activity intensity dynamics Commuting flow
Online Access:	http://www.sciencedirect.com/science/article/pii/S156984322300434X

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