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.

中文翻译：

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Bikeshare站点区域类型，以预测系统扩展的站点级别乘客数量

停放的共享单车系统的不断引入和扩展以及公开的来源地数据为共享单车研究开辟了令人兴奋的途径。作为回应，最近的大量研究检查了社会人口统计学的决定因素以及系统乘客的安全或自然环境威慑因素。越来越多的分散空间数据也刺激了最近的研究呼吁，这些研究旨在扩展这些上下文数据的效用，以模拟自行车需求和出行方式。随着规划者和经营者试图将共享单车服务扩展到服务不足的地区，需要提供一种数据驱动的共享单车使用空间动态的理解。华盛顿特区的这项研究 都会区的Capital Bikeshare（CaBi）程序通过执行潜在类聚类分析以根据一组土地开发模式，城市设计和交通基础设施特征的变化来识别五种Bikeshare车站区域类型，从而回答了这一问题。这种类型被集成到一个计划应用程序中，该应用程序探索将系统扩展到附近辖区的潜力，并预测现有站点和拟议站点位置之间相关的旅行潜力。