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...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2024-02-01
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| Series: | International Journal of Applied Earth Observations and Geoinformation |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S156984322300434X |