  Mobility-on-Demand (MoD) systems require load balancing to maintain
consistent service across regions with uneven demand subject to time-varying
traffic conditions. The load-balancing objective is to jointly minimize the
fraction of lost customer requests due to vehicle unavailability and the
fraction of time when vehicles drive empty during load balancing operations. In
order to bypass the intractability of a globally optimal solution to this
stochastic dynamic optimization problem, we propose a parametric
threshold-based control driven by the known relative abundance of vehicles
available in and en route to each region. This is still a difficult parametric
optimization problem for which one often resorts to trial-and-error methods
where multiple sample paths are generated through simulation or from actual
data under different parameter settings. In contrast, this paper utilizes
concurrent estimation methods to simultaneously construct many sample paths
from a single nominal sample path. The performance of the parametric controller
for intermediate size systems is compared to that of a simpler single-parameter
controller, a state-blind static controller, a policy of no control, and a
theoretically-derived lower bound. Simulation results show the value of state
information in improving performance.
