Robotic load balancing for mobility-on-demand systems

Authors:
Marco Pavone;Stephen L Smith;Emilio Frazzoli;Daniela Rus
Affiliations:
Department of Aeronautics and Astronautics, Stanford University, Stanford, CA, USA;Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, ON, Canada;Laboratory for Information and Decision Systems, Aeronautics and Astronautics Department, Massachusetts Institute of Technology, Cambridge, MA, USA;Computer Science and Artificial Intelligence Laboratory, Electrical Engineering and Computer Science Department, Massachusetts Institute of Technology, Cambridge, MA, USA
Venue:
International Journal of Robotics Research
Year:
2012

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Abstract

In this paper we develop methods for maximizing the throughput of a mobility-on-demand urban transportation system. We consider a finite group of shared vehicles, located at a set of stations. Users arrive at the stations, pickup vehicles, and drive (or are driven) to their destination station where they drop-off the vehicle. When some origins and destinations are more popular than others, the system will inevitably become out of balance: vehicles will build up at some stations, and become depleted at others. We propose a robotic solution to this rebalancing problem that involves empty robotic vehicles autonomously driving between stations. Specifically, we utilize a fluid model for the customers and vehicles in the system. Then, we develop a rebalancing policy that lets every station reach an equilibrium in which there are excess vehicles and no waiting customers and that minimizes the number of robotic vehicles performing rebalancing trips. We show that the optimal rebalancing policy can be found as the solution to a linear program. We use this solution to develop a real-time rebalancing policy which can operate in highly variable environments. Finally, we verify policy performance in a simulated mobility-on-demand environment and in hardware experiments.