  A free-floating bike-sharing system (FFBSS) is a dockless rental system where
an individual can borrow a bike and returns it anywhere, within the service
area. To improve the rental service, available bikes should be distributed over
the entire service area: a customer leaving from any position is then more
likely to find a near bike and then to use the service. Moreover, spreading
bikes among the entire service area increases urban spatial equity since the
benefits of FFBSS are not a prerogative of just a few zones. For guaranteeing
such distribution, the FFBSS operator can use vans to manually relocate bikes,
but it incurs high economic and environmental costs. We propose a novel
approach that exploits the existing bike flows generated by customers to
distribute bikes. More specifically, by envisioning the problem as an Influence
Maximization problem, we show that it is possible to position batches of bikes
on a small number of zones, and then the daily use of FFBSS will efficiently
spread these bikes on a large area. We show that detecting these zones is
NP-complete, but there exists a simple and efficient $1-1/e$ approximation
algorithm; our approach is then evaluated on a dataset of rides from the
free-floating bike-sharing system of the city of Padova.
