We propose a framework for data-driven pricing in shared vehicle systems
(such as bikesharing and carsharing) in which customers can pick up and drop
off vehicles in different locations. This framework provides efficient
algorithms with rigorous approximation guarantees for a wide class of objective
functions (including welfare and revenue), and under a variety of constraints
on the prices. An interesting class of constraints accommodated by this
framework includes multi-objective settings in which the goal is to maximize
some objective function subject to some lower bound on another objective
function. An important representative of this class is the Ramsey pricing
problem, i.e. maximize revenue subject to some lower bound on welfare.
Compared to traditional item-pricing problems, pricing in shared vehicle
systems is more challenging due to network externalities, wherein setting
prices at one demand node may affect the supply at each other node in the
network. To capture the system dynamics, we use a closed queueing model in
which exogenous demand (obtained from data) can be modulated through pricing.
We achieve our approximation guarantees by first projecting the problem to an
infinite-supply setting, deriving optimal prices in this limit, and then
bounding the performance of these prices in the finite-vehicle system. Our
infinite-to-finite supply reduction is of independent interest since it holds
for a broader class of objective functions, and applies even more generally
than the pricing problems that we consider.