文章基本信息

标题：A bikeshare station area typology to forecast the station-level ridership of system expansion
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作者：Steven R. Gehrke ; Timothy F. Welch
期刊名称：Journal of Transport and Land Use
印刷版ISSN：1938-7849
出版年度：2019
卷号：12
期号：1
DOI：10.5198/jtlu.2019.1395
语种：English
出版社：University of Minnesota * Department of Civil, Environmental, and Geo-Engineering
摘要：The continuous introduction and expansion of docked bikeshare systems with publicly available origin-destination data have opened exciting avenues for bikeshare research. In response, a flux of recent studies has examined the sociodemographic determinants and safety or natural environment deterrents of system ridership. An increasing abundance of disaggregate spatial data has also spurred recent calls for research aimed at extending the utility of these contextual data to model bikeshare demand and trip patterns. As planners and operators seek to expand bikeshare services into underserved areas, a need exists to provide a data-driven understanding of the spatial dynamics of bikeshare use. This study of the Washington, DC, metro region’s Capital Bikeshare (CaBi) program answers this call by performing a latent class cluster analysis to identify five bikeshare station area types based on variation in a set of land development pattern, urban design, and transportation infrastructure features. This typology is integrated into a planning application exploring the potential for system expansion into nearby jurisdictions and forecasting the associated trip-making potential between existing and proposed station locations.
关键词：bikeshare;built environment;latent class analysis;bike ridership;machine learning