  In this paper we present a queueing network approach to the problem of
routing and rebalancing a fleet of self-driving vehicles providing on-demand
mobility within a capacitated road network. We refer to such systems as
autonomous mobility-on-demand systems, or AMoD. We first cast an AMoD system
into a closed, multi-class BCMP queueing network model. Second, we present
analysis tools that allow the characterization of performance metrics for a
given routing policy, in terms, e.g., of vehicle availabilities, and first and
second order moments of vehicle throughput. Third, we propose a scalable method
for the synthesis of routing policies, with performance guarantees in the limit
of large fleet sizes. Finally, we validate our theoretical results on a case
study of New York City. Collectively, this paper provides a unifying framework
for the analysis and control of AMoD systems, which subsumes earlier Jackson
and network flow models, provides a quite large set of modeling options (e.g.,
the inclusion of road capacities and general travel time distributions), and
allows the analysis of second and higher-order moments for the performance
metrics.
