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Published April 6, 2021 | Version v1
Conference paper Open

Profiling the Dynamic Pattern of Bike-sharing Stations: a case study of Citi Bike in New York City

  • 1. Geographic Data Science Lab, Geography and Planning, University of Liverpool, Liverpool, L69 7ZT

Description

This research applies a hierarchical k-means clustering method on the TF-IDF weighted 2019 cycling transactions from the Citi Bike bike-sharing system operating in New York City, with the primary goal of investigating the spatiotemporal usage pattern of its docking points. With a particular focus on bike-sharing stations in Manhattan, we classify 504 stations into four main clusters featuring heterogeneous dynamic usages, including leisure-oriented, residential- oriented, workplace-oriented, and off-peak oriented. We interpret each cluster based on their salient characteristics and anticipate possible future directions of this work.

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