This study explores factors contributing to the uneven success of past expansions of dock-based public bikesharing systems, in which middle- and - higher-income neighborhoods have tended to benefit considerably more than poorer neighborhoods. After a review of the differing performance of the three phases of expansion by Chicago's Divvy bikeshare system, this study uses multivariate adaptive regression splining (MARS) to select among more than 100 community- and station-level factors to explain variations in Divvy system usage at the station level. MARS demonstrates that neighborhood racial and ethnic diversity, proportion of condominium units, and job accessibility to public transit are strongly and positively correlated with total annual station trips, whereas percentage unemployed, average distance to Divvy stations, and percentage of residential foreclosures are negatively correlated. Model results are compared with those of earlier studies to foster insights into ways to more accurately predict the use of bikesharing systems across urban neighborhoods.

本研究探讨了导致基于码头的公共自行车共享系统过去扩张不均衡成功的因素，其中中高收入社区往往比贫困社区受益更多。在回顾了芝加哥 Divvy 共享单车系统三个扩展阶段的不同表现后，本研究使用多元自适应回归样条 (MARS) 从 100 多个社区和站点级别的因素中进行选择，以解释 Divvy 系统使用的变化车站级别。MARS 表明，邻里种族和民族多样性、公寓单位的比例以及公共交通的工作可及性与年度车站总出行次数呈强正相关，而失业百分比、到 Divvy 车站的平均距离、和住宅止赎百分比呈负相关。将模型结果与早期研究的结果进行比较，以深入了解更准确地预测城市社区中自行车共享系统的使用情况。