Given the burgeoning growth in bikeshare system installations and their growing adoption for trip making, it is important to develop modeling frameworks to understand bikeshare demand flows in the system. The current study examines two choice dimensions for capturing the system level bikeshare system demand: (1) total station level demand and (2) distribution of bike flows from an origin station across the network. A linear mixed model is used to estimate the first choice and Multiple Discrete Continuous Extreme Value (MDCEV) model is used to analyze the latter. The data is drawn from the New York City bikeshare system (CitiBike) for six months (January through June 2017). For our analysis, we examine demand and distribution patterns on a weekly basis controlling for a host of independent variables (trip, socio-demographics, bicycle infrastructure, land use and built environment, temporal and weather). Model validation exercise results revealed that the proposed model performs well for low demand destinations. A policy exercise evaluating destination choice behavior demonstrated how the impact of distance is compensated by additional bicycling infrastructure in the farther locations. The results from the study help bikesharing system planners and operators to better evaluate and improve bikeshare systems.

鉴于Bikeshare系统安装的迅速增长及其在出行中的采用不断增加，开发建模框架以了解系统中Bikeshare需求流非常重要。当前的研究检查了两个选择维度，以捕获系统级单车共享系统需求：（1）总站级需求；（2）来自原始站点的自行车流量在网络中的分布。线性混合模型用于估计第一选择，多元离散连续极值（MDCEV）模型用于分析后者。数据来自纽约市单车共享系统（CitiBike），为期六个月（2017年1月至2017年6月）。在我们的分析中，我们每周检查一次需求和分配方式，以控制一系列独立变量（行程，社会人口统计学，自行车基础设施，土地使用和建筑环境，时间和天气）。模型验证练习结果表明，所提出的模型对于低需求目的地表现良好。一项评估目的地选择行为的政策演习表明，在更远的地方如何通过附加的自行车基础设施来补偿距离的影响。该研究的结果可帮助共享单车系统规划者和运营商更好地评估和改进共享单车系统。