  Shared mobility redefines urban transportation, offering economic and
environmental benefits by reducing pollution and urban congestion. However, in
the post-pandemic era, the shared mobility sector is grappling with a crisis of
trust, particularly concerning passenger hesistancy towards shared
transportation options. To address these problems, in this paper we take social
network into consideration and propose a novel carpooling matching framework
based on graph neural network and reinforcement learning,increasing the
carpooling rate to 48% and reducing the average delay time to 6.1 minutes and
average detour distance to 2.8km. Furthermore, we introduce an innovative
metric, termed 'tolerance' for mobility scheduling models to effectively
quantify users' sensitivity to social distancing. We conduct a sensitivity
analysis to demonstrate that our model offers a viable approach to amplify the
benefits, delivering resilient strategies for the advancement and proliferation
of shared mobility incentives.
