  This case-study aims at a comparison of the service quality of time-tabled
buses as compared to on-demand ridepooling cabs in the late evening hours in
the city of Wuppertal, Germany. To evaluate the service quality of ridepooling
as compared to bus services, and to simulate bus rides during the evening
hours, transport requests are generated using a predictive simulation. To this
end, a framework in the programming language R is created, which automatically
combines generalized linear models for count regression to model the demand at
each bus stop. Furthermore, we use classification models for the prediction of
trip destinations. To solve the resulting dynamic dial-a-ride problem, a
rolling-horizon algorithm based on the iterative solution of Mixed-Integer
Linear Programming Models (MILP) is used. A feasible-path heuristic is used to
enhance the performance of the algorithm in presence of high request densities.
This allows an estimation of the number of cabs needed depending on the weekday
to realize the same or a better general service quality as the bus system.
