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arXiv · 2208.08150

Capturing usage patterns in bike sharing system via multilayer network fused Lasso

Yunjin Choi·Haeran Cho·Hyelim Son
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Abstract

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.

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Yunjin Choi, Haeran Cho, Hyelim Son. 2022-08-17. Capturing usage patterns in bike sharing system via multilayer network fused Lasso. https://arxiv.org/abs/2208.08150

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