  Conventional Public Transport (PT) is based on fixed lines, running with
routes and schedules determined a-priori. In low-demand areas, conventional PT
is inefficient. Therein, Mobility on Demand (MoD) could serve users more
efficiently and with an improved quality of service (QoS). The idea of
integrating MoD into PT is therefore abundantly discussed by researchers and
practitioners, mainly in the form of adding MoD on top of PT. Efficiency can be
instead gained if also conventional PT lines are redesigned after integrating
MoD in the first or last mile. In this paper we focus on this re-design
problem. We devise a bilevel optimization problem where, given a certain
initial design, the upper level determines stop selection and frequency
settings, while the lower level routes a fleet of MoD vehicles. We propose a
solution method based on Particle Swarm Optimization (PSO) for the upper level,
while we adopt Large Neighborhood Search (LNS) in the lower level. Our solution
method is computationally efficient and we test it in simulations with up to
10k travel requests. Results show important operational cost savings obtained
via appropriately reducing the conventional PT coverage after integrating MoD,
while preserving QoS.
