Revisiting the Modifiable Areal Unit Problem in Deep Traffic Prediction with Visual Analytics
Deep learning methods are being increasingly used for urban traffic prediction where spatiotemporal traffic data is aggregated into sequentially organized matrices that are then fed into convolution-based residual neural networks. However, the widely known modifiable areal unit problem within such a...
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Bibliographische Detailangaben
| Veröffentlicht in: | IEEE transactions on visualization and computer graphics. - 1996. - 27(2021), 2 vom: 28. Feb., Seite 839-848
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| 1. Verfasser: |
Zeng, Wei
(VerfasserIn) |
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| Weitere Verfasser: |
Lin, Chengqiao,
Lin, Juncong,
Jiang, Jincheng,
Xia, Jiazhi,
Turkay, Cagatay,
Chen, Wei |
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| Format: | Online-Aufsatz
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| Sprache: | English |
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| Veröffentlicht: |
2021
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| Zugriff auf das übergeordnete Werk: | IEEE transactions on visualization and computer graphics
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| Schlagworte: | Journal Article |
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