  This study explores the design of an On-Demand Multimodal Transit System
(ODMTS) that includes segmented mode switching models that decide whether
potential riders adopt the new ODMTS or stay with their personal vehicles. It
is motivated by the desire of transit agencies to design their network by
taking into account both existing and latent demand, as quality of service
improves. The paper presents a bilevel optimization where the leader problem
designs the network and each rider has a follower problem to decide her best
route through the ODMTS. The bilevel model is solved by a decomposition
algorithm that combines traditional Benders cuts with combinatorial cuts to
ensure the consistency of mode choices by the leader and follower problems. The
approach is evaluated on a case study using historical data from Ann Arbor,
Michigan, and a user choice model based on the income levels of the potential
transit riders.
