  This paper studies how to integrate rider mode preferences into the design of
On-Demand Multimodal Transit Systems (ODMTS). It is motivated by a common worry
in transit agencies that an ODMTS may be poorly designed if the latent demand,
i.e., new riders adopting the system, is not captured. The paper proposes a
bilevel optimization model to address this challenge, in which the leader
problem determines the ODMTS design, and the follower problems identify the
most cost efficient and convenient route for riders under the chosen design.
The leader model contains a choice model for every potential rider that
determines whether the rider adopts the ODMTS given her proposed route. To
solve the bilevel optimization model, the paper proposes an exact decomposition
method that includes Benders optimal cuts and nogood cuts to ensure the
consistency of the rider choices in the leader and follower problems. Moreover,
to improve computational efficiency, the paper proposes upper bounds on trip
durations for the follower problems and valid inequalities that strenghten the
nogood cuts.
  The proposed method is validated using an extensive computational study on a
real data set from AAATA, the transit agency for the broader Ann Arbor and
Ypsilanti region in Michigan. The study considers the impact of a number of
factors, including the price of on-demand shuttles, the number of hubs, and
accessibility criteria. The designed ODMTS feature high adoption rates and
significantly shorter trip durations compared to the existing transit system
and highlight the benefits in accessibility for low-income riders. Finally, the
computational study demonstrates the efficiency of the decomposition method for
the case study and the benefits of computational enhancements that improve the
baseline method by several orders of magnitude.
