  This paper addresses the pressing challenge of urban mobility in the context
of growing urban populations, changing demand patterns for urban mobility, and
emerging technologies like Mobility-on-Demand (MoD) platforms and Autonomous
Vehicle (AV). As urban areas swell and demand pattern changes, the integration
of Autonomous Mobility-on-Demand (AMoD) systems with existing public transit
(PT) networks presents great opportunities to enhancing urban mobility. We
propose a novel optimization framework for solving the Transit-Centric
Multimodal Urban Mobility with Autonomous Mobility-on-Demand (TCMUM-AMoD) at
scale. The system operator (public transit agency) determines the network
design and frequency settings of the PT network, fleet sizing and allocations
of AMoD system, and the pricing for using the multimodal system with the goal
of minimizing passenger disutility. Passengers' mode and route choice behaviors
are modeled explicitly using discrete choice models. A first-order
approximation algorithm is introduced to solve the problem at scale. Using a
case study in Chicago, we showcase the potential to optimize urban mobility
across different demand scenarios. To our knowledge, ours is the first paper to
jointly optimize transit network design, fleet sizing, and pricing for the
multimodal mobility system while considering passengers' mode and route
choices.
