Effects of Dynamic and Stochastic Travel Times on the Operation of Mobility-on-Demand Services

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. Nevertheless, once the service is confirmed, it is imperative that those customers remain part of the assignment. Hence, damage control measures have to counteract this effect. This research integrates an elaborate simulation framework for MoD services with a microscopic traffic simulation to consider dynamic and stochastic network travel times. Results from a case study for Munich, Germany show, 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 pickup 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.