Vehicle Routing for Shared-Mobility Systems with Time-Varying Demand

This work considers mobility systems in which a shared fleet of self-driving vehicles is used to transport passengers. More specifically, we focus on policies to route both passenger-filled and empty vehicles when the travel demand is time-varying. In this setting, we argue that metrics, such as the cost to relocate empty vehicles, which are well-defined in a stand-alone capacity under steady-state conditions, now make sense only within a framework that reflects inherent tradeoffs with other metrics, e. g., the fleet size and the quality of service provided. As a first step toward developing a general theory of time-varying, shared-mobility systems, we provide an optimization framework that models passengers and vehicles as continuous fluids, and their movement as fluid flows. The model is used to develop some initial performance results related to the minimum number of vehicles required to avoid passenger queueing. Finally, simulation results of a hypothetical shared mobility system based in Singapore demonstrate how a fleet manager could use our optimization approach to select a vehicle routing policy.