Capturing usage patterns in bike sharing system via multilayer network fused Lasso
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arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2022
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| Acceso en línea: | |
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| _version_ | 1866910575356805120 |
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| author | Choi, Yunjin Cho, Haeran Son, Hyelim |
| author_facet | Choi, Yunjin Cho, Haeran Son, Hyelim |
| contents | Data collected from a bike-sharing system exhibit complex temporal and spatial features. We analyze shared-bike usage data collected in three large cities at the level of individual stations, accounting for station-specific behavior and covariate effects. For this, we adopt a penalized regression approach with a multilayer network fused Lasso penalty. These fusion penalties are imposed on networks which embed spatio-temporal linkages, and capture the homogeneity in bike usage that is attributed to intricate spatio-temporal features without arbitrarily partitioning the data. On the real-life datasets, we demonstrate that the proposed approach yields competitive predictive performance and provides a new interpretation of the data. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2208_08150 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Capturing usage patterns in bike sharing system via multilayer network fused Lasso Choi, Yunjin Cho, Haeran Son, Hyelim Applications Data collected from a bike-sharing system exhibit complex temporal and spatial features. We analyze shared-bike usage data collected in three large cities at the level of individual stations, accounting for station-specific behavior and covariate effects. For this, we adopt a penalized regression approach with a multilayer network fused Lasso penalty. These fusion penalties are imposed on networks which embed spatio-temporal linkages, and capture the homogeneity in bike usage that is attributed to intricate spatio-temporal features without arbitrarily partitioning the data. On the real-life datasets, we demonstrate that the proposed approach yields competitive predictive performance and provides a new interpretation of the data. |
| title | Capturing usage patterns in bike sharing system via multilayer network fused Lasso |
| topic | Applications |
| url | https://arxiv.org/abs/2208.08150 |