  Balancing passenger demand and vehicle availability is crucial for ensuring
the sustainability and effectiveness of urban transportation systems. To
address this challenge, we propose a novel hierarchical strategy for the
efficient distribution of empty vehicles in urban areas. The proposed approach
employs a data-enabled predictive control algorithm to develop a high-level
controller, which guides the inter-regional allocation of idle vehicles. This
algorithm utilizes historical data on passenger demand and vehicle supply in
each region to construct a non-parametric representation of the system,
enabling it to determine the optimal number of vehicles to be repositioned or
retained in their current regions without modeling the system. At the low
level, a coverage control-based controller is designed to provide
inter-regional position guidance, determining the desired road intersection
each vehicle should target. With the objective of optimizing area coverage, it
aligns the vehicle distribution with the demand across different districts
within a single region. The effectiveness of the proposed method is validated
through simulation experiments on the real road network of Shenzhen, China. The
integration of the two layers provides better performance compared to applying
either layer in isolation, demonstrating its potential to reduce passenger
waiting time and answer more requests, thus promoting the development of more
efficient and sustainable transportation systems.
