  Mobility-on-Demand (MoD) services have been an active research topic in
recent years. Many studies focused on developing control algorithms to supply
efficient services. To cope with a large search space to solve the underlying
vehicle routing problem, studies usually apply hard time-constraints on pick-up
and drop-off while considering static network travel times to reduce
computational time. As travel times in real street networks are dynamic and
stochastic, assigned routes considered feasible by the control algorithm in one
time step might become infeasible in the next. Since once assigned and
confirmed, customers should still be part of the solution, damage control is
necessary to counteract this effect. In this study, a detailed simulation
framework for MoD services is coupled with a microscopic traffic simulation to
create dynamic and stochastic travel times, and tested in a case study for
Munich, Germany. Results showed that the combination of inaccurate travel time
estimation and damage control strategies for infeasible routes deteriorates the
performance of MoD services -- hailing and pooling -- significantly. Moreover,
customers suffer from unreliable pick-up time and travel time estimations.
Allowing re-assignments of initial vehicle schedules according to updated
system states helps to restore system efficiency and reliability, but only to a
minor extent.
