  The design of integrated mobility-on-demand services requires jointly
considering the interactions between traveler choice behavior and operators'
operation policies to design a financially sustainable pricing scheme. However,
most existing studies focus on the supply side perspective, disregarding the
impact of customer choice behavior in the presence of co-existing transport
networks. We propose a modeling framework for dynamic integrated
mobility-on-demand service operation policy evaluation with two service
options: door-to-door rideshare and rideshare with transit transfer. A new
constrained dynamic pricing model is proposed to maximize operator profit,
taking into account the correlated structure of different modes of transport.
User willingness to pay is considered as a stochastic constraint, resulting in
a more realistic ticket price setting while maximizing operator profit. Unlike
most studies, which assume that travel demand is known, we propose a demand
learning process to calibrate customer demand over time based on customers'
historical purchase data. We evaluate the proposed methodology through
simulations under different scenarios on a test network by considering the
interactions of supply and demand in a multimodal market. Different scenarios
in terms of customer arrival intensity, vehicle capacity, and the variance of
user willingness to pay are tested. Results suggest that the proposed
chance-constrained assortment price optimization model allows increasing
operator profit while keeping the proposed ticket prices acceptable.
