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

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Aug 16th, 12:00 AM

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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