  Bike-sharing transportation systems have been well studied from a top-down
viewpoint, either for an optimal conception of the system, or for a better
statistical understanding of their working mechanisms in the aim of the
optimization of the management strategy. Yet bottom-up approaches that could
include behavior of users have not been well studied so far. We propose an
agent-based model for the short time evolution of a bike-sharing system, with a
focus on two strategical parameters that are the role of the quantity of
information users have on the all system and the propensity of user to walk
after having dropped their bike. We implement the model in a general way so it
is applicable to every system as soon as data are available in a certain
format. The model of simulation is parametrized and calibrated on processed
real time-series of bike movements for the system of Paris. After showing the
robustness of the simulations by validating internally and externally the
model, we are able to test different user-based strategies for an increase of
the level of service. In particular, we show that an increase of user
information can have significant impact on the homogeneity of repartition of
bikes in docking stations, and, what is important for a future implementation
of the strategy, that an action on only 30% of regular users is enough to
obtain most of the possible amelioration.
