Understanding the spatiotemporal patterns of public bicycle usage: A case study of Hangzhou, China

Abstract

Bicycle sharing has become an increasingly popular transportation option for promoting non-motorized transport and reducing automobile dependence. Hangzhou, China currently has the largest bicycle-sharing program in the world; however, this program faces challenges from the dynamic and unevenly distributed demand for public bicycles and docking spaces. In this study, we propose an approach for understanding the spatiotemporal patterns of bicycle sharing activities by analyzing bicycle usage data streams from approximately 1500 bicycle stations collected at one-minute intervals for the entire month of October 2012. To extract useful information from this large dataset, we extract the spatiotemporal dynamics of public bicycle usage and apply a fuzzy clustering algorithm to cluster bicycle stations into various types based on temporal distribution of usage over a day. We then employ spatial regression models to assess the relationship between the type of stations and corresponding locational characteristics after controlling for the spatial dependence of neighboring stations. The proposed approach offers useful insights that could support the operation of bicycle sharing programs. The study also demonstrates the enormous potential of mining emergent big datasets, such as urban sensing data streams, for improving transportation infrastructure management and urban planning practices.

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