Bike enthusiasts argue that bikesharing programs can be an important element of sustainable mobility planning in the urban cores of large metropolitan areas. However, the objective long-term impact of bikesharing on reducing auto-dependence is not well-examined, as prior studies have tended to rely on self-reported subjective mode substitution effects. We use a unique longitudinal dataset containing millions of geo-referenced vehicle registrations and odometer readings in Massachusetts over a six-year period - the Massachusetts Vehicle Census - to examine the causal impact of bikesharing on various metrics of auto-dependence in the inner core of Metro Boston. The difference-in-differences (DiD) framework is extended to accommodate spatial spillover effects with the inclusion of a spatial autoregressive lag leading to the spatial DiD (SpDiD) model. We also account for seasonal variation in bikeshare operations, where several stations are shut down for the winter months, by setting up a dynamic treatment definition. We find that a new bikeshare station reduces vehicle ownership per household by 2.2%, vehicle miles traveled per person by 3.3%, and per-capita vehicular GHG emissions by 2.9%. We also find strong evidence to support the use of bikesharing as a first/last-mile connector to mass transit. Auto-dependence reductions are around 10% (more than thrice as high as average) where bikeshare connections to transit stations are less than one kilometer long. Finally, we find that vehicle ownership reductions are almost immediate and last up to a year, while vehicle use and emission reductions are lagged over 1.5 years. These sizeable and measurable auto-substitution effects do support some of the claims of bikesharing advocates. These findings are especially important in the post-COVID-19 era, as cities strive to counter the pandemic-inspired safety skepticism about non-car travel.

自行车爱好者认为，共享自行车计划可能是大都市区城市核心区可持续交通规划的重要组成部分。然而，由于先前的研究倾向于依靠自我报告的主观模式替代效应，因此未充分检验自行车共享对减少自动依赖的客观长期影响。我们使用了一个独特的纵向数据集，其中包含六年内马萨诸塞州数百万的地理参考车辆注册和里程表读数-马萨诸塞州汽车普查-考察单车共享对波士顿大都会核心区各种自动依赖指标的因果影响。差异差异（DiD）框架经过扩展以适应空间溢出效应，并包含导致空间DiD（SpDiD）模型的空间自回归滞后。我们还考虑了共享单车运营的季节性变化，在冬季，通过设置动态治疗定义，几个站点被关闭。我们发现，新的共享单车站可将每户家庭的汽车拥有量减少2.2％，将每人的行驶里程减少3.3％，并将人均汽车温室气体排放量减少2.9％。我们还找到了强有力的证据来支持将共享单车作为大众运输的第一/最后一英里连接器。当与公交车站的单车共享距离不到一公里时，自动依赖的减少量约为10％（是平均水平的三倍还多）。最后，我们发现车辆拥有量的减少几乎是即时的，并且可持续长达一年，而车辆使用和排放量的减少则滞后了1.5年。这些巨大且可测量的自动替换效果确实支持了共享单车倡导者的某些主张。这些发现在后COVID-19时代尤其重要，因为各城市都在努力应对大流行引发的对非汽车旅行的怀疑。这些巨大且可测量的自动替换效果确实支持了共享单车倡导者的某些主张。这些发现在后COVID-19时代尤其重要，因为各城市都在努力应对大流行引发的对非汽车旅行的怀疑。这些巨大且可测量的自动替换效果确实支持了共享单车倡导者的某些主张。这些发现在后COVID-19时代尤其重要，因为各城市都在努力应对大流行引发的对非汽车旅行的怀疑。