  The goal of this paper is to develop a modeling framework that captures the
inter-decision dynamics between mobility service providers (MSPs) and travelers
that can be used to optimize and analyze policies/regulations related to MSPs.
To meet this goal, the paper proposes a tri-level mathematical programming
model with a public-sector decision maker (regulator) at the highest level, the
MSP in the middle level, and travelers at the lowest level. The regulator aims
to maximize social welfare via implementing regulations, policies, plans,
transit service designs, etc. The MSP aims to maximize profit by adjusting its
service designs. Travelers aim to maximize utility by changing their modes and
routes. The travelers' decisions depend on the regulator and MSP's decisions
while the MSP decisions themselves depend on the regulator's decisions. To
solve the tri-level mathematical program, the study employs Bayesian
optimization (BO) within a simulation-optimization solution approach. At the
lowest level, the solution approach includes an agent-based transportation
system simulation model to capture travelers' behavior subject to specific
decisions made by the regulator and MSP. The agent-based transportation
simulation model includes a mode choice model, a road network, a transit
network, and an MSP providing automated mobility-on-demand (AMOD) service with
shared rides. The modeling and solution approaches are applied to Munich,
Germany in order to validate the model. The case study investigates the tolls
and parking costs the city administration should set, as well as changes in the
public transport budget and a limitation of the AMOD fleet size. Best policy
settings are derived for two social welfare definitions, in both of which the
AMOD fleet size is not regulated as the shared-ride AMOD service provides
significant value to travelers in Munich.
