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Graph convolutional network approach applied to predict hourly bike-sharing demands considering spatial, temporal, and global effects
Solving the supply–demand imbalance is the most crucial issue for stable implementation of a public bike-sharing system. This gap can be reduced by increasing the accuracy of demand prediction by considering spatial and temporal properties of bike demand. However, only a few attempts have been made...
| Autores principales: | , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Public Library of Science
2019
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6746382/ https://www.ncbi.nlm.nih.gov/pubmed/31525227 http://dx.doi.org/10.1371/journal.pone.0220782 |