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Paper Type
Complete
Abstract
We investigate data-driven strategies for planning the locations of bike-sharing system stations, uniquely considering both competition from nearby stations and the complementary influence of stations within a bike trip's target area. Our investigation is based on a dataset of over eight million entries from three jurisdictions affiliated with a leading German bike-sharing provider. To evaluate our approach, we employ a spatial out-of-sample technique on this dataset.
Paper Number
1845
Recommended Citation
Sengewald, Julian and Lackes, Richard, "Bike-Sharing Station Placement: Spatial Analysis and Data Mining of Network Design Characteristics" (2024). AMCIS 2024 Proceedings. 10.
https://aisel.aisnet.org/amcis2024/dsa/dsa/10
Bike-Sharing Station Placement: Spatial Analysis and Data Mining of Network Design Characteristics
We investigate data-driven strategies for planning the locations of bike-sharing system stations, uniquely considering both competition from nearby stations and the complementary influence of stations within a bike trip's target area. Our investigation is based on a dataset of over eight million entries from three jurisdictions affiliated with a leading German bike-sharing provider. To evaluate our approach, we employ a spatial out-of-sample technique on this dataset.
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