  Automated vehicles (AVs) are expected to be beneficial for Mobility-on-Demand
(MoD), thanks to their ability of being globally coordinated. To facilitate the
steady transition towards full autonomy, we consider the transition period of
AV deployment, whereby an MoD system operates a mixed fleet of automated
vehicles (AVs) and human-driven vehicles (HVs). In such systems, AVs are
centrally coordinated by the operator, and the HVs might strategically respond
to the coordination of AVs. We devise computationally tractable strategies to
coordinate mixed fleets in MoD systems. Specifically, we model an MoD system
with a mixed fleet using a Stackelberg framework where the MoD operator serves
as the leader and human-driven vehicles serve as the followers. We develop two
models: 1) a steady-state model to analyze the properties of the problem and
determine the planning variables (e.g., compensations, prices, and the fleet
size of AVs), and 2) a time-varying model to design a real-time coordination
algorithm for AVs. The proposed models are validated using a case study
inspired by real operational data of a MoD service in Singapore. Results show
that the proposed algorithms can significantly improve system performance.
