  Mobility-on-Demand (MoD) services, such as taxi-like services, are promising
applications. Rebalancing the vehicle locations against customer requests is a
key challenge in the services because imbalance between the two worsens service
quality (e.g., longer waiting times). Previous work would be hard to apply to
large-scale MoD services because of the computational complexity. In this
study, we develop a scalable approach to optimize rebalancing policy in stages
from coarse regions to fine regions hierarchically. We prove that the
complexity of our method decreases exponentially with increasing number of
layers, while the error is bounded. We numerically confirmed that the method
reduces computational time by increasing layers with a little extra travel time
using a real-world taxi trip dataset.
