  As a rapidly expanding service, bike sharing is facing severe problems of
bike over-supply and demand fluctuation in many Chinese cities. This study
develops a large-scale method to determine the minimum fleet size under
uncertainty, based on the bike sharing data of millions of trips in Nanjing. It
is found that the algorithm of minimizing fleet size under the
incomplete-information scenario is effective in handling future uncertainty.
For a dockless bike sharing system, supplying 14.5% of the original fleet could
meet 96.8% of trip demands. Meanwhile, the results suggest that providing a
integrated service platform that integrates multiple companies can
significantly reduce the total fleet size by 44.6%. Moreover, in view of the
COVID-19 pandemic, this study proposes a social distancing policy that
maintains a suitable usage interval. These findings provide useful insights for
improving the resource efficiency and operational service of bike sharing and
shared mobility.
