  The increasing availability and adoption of shared vehicles as an alternative
to personally-owned cars presents ample opportunities for achieving more
efficient transportation in cities. With private cars spending on the average
over 95\% of the time parked, one of the possible benefits of shared mobility
is the reduced need for parking space. While widely discussed, a systematic
quantification of these benefits as a function of mobility demand and sharing
models is still mostly lacking in the literature. As a first step in this
direction, this paper focuses on a type of private mobility which, although
specific, is a major contributor to traffic congestion and parking needs,
namely, home-work commuting. We develop a data-driven methodology for
estimating commuter parking needs in different shared mobility models,
including a model where self-driving vehicles are used to partially compensate
flow imbalance typical of commuting, and further reduce parking infrastructure
at the expense of increased traveled kilometers. We consider the city of
Singapore as a case study, and produce very encouraging results showing that
the gradual transition to shared mobility models will bring tangible reductions
in parking infrastructure. In the future-looking, self-driving vehicle
scenario, our analysis suggests that up to 50\% reduction in parking needs can
be achieved at the expense of increasing total traveled kilometers of less than
2\%.
