  This paper studies optimal pricing and rebalancing policies for Autonomous
Mobility-on-Demand (AMoD) systems. We take a macroscopic planning perspective
to tackle a profit maximization problem while ensuring that the system is
load-balanced. We begin by describing the system using a dynamic fluid model to
show the existence and stability of an equilibrium (i.e., load balance) through
pricing policies. We then develop an optimization framework that allows us to
find optimal policies in terms of pricing and rebalancing. We first maximize
profit by only using pricing policies, then incorporate rebalancing, and
finally we consider whether the solution is found sequentially or jointly. We
apply each approach on a data-driven case study using real taxi data from New
York City. Depending on which benchmarking solution we use, the joint problem
(i.e., pricing and rebalancing) increases profits by 7% to 40%
