Rebalancing the rebalancers: Optimally routing vehicles and drivers in mobility-on-demand systems
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Frazzoli_Rebalancing the rebalancers.pdf
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Author(s)
Smith, Stephen L.
Pavone, Marco
Schwager, Mac
Frazzoli, Emilio
Rus, Daniela L.
Date Issued
June 2013
Journal
Proceedings of the 2013 American Control Conference
Publisher
American Automatic Control Council
Citation
Smith, Stephen L., Marco Pavone, Mac Schwager, Emilio Frazzoli, and Daniela Rus. "Rebalancing the rebalancers: Optimally routing vehicles and drivers in mobility-on-demand systems." 2013 American Control Conference (ACC) Washington, DC, USA, June 17-19, 2013. American Automatic Control Council.
Version
Original manuscript
Abstract
In this paper we study rebalancing strategies for a mobility-on-demand urban transportation system blending customer-driven vehicles with a taxi service. In our system, a customer arrives at one of many designated stations and is transported to any other designated station, either by driving themselves, or by being driven by an employed driver. When some origins and destinations are more popular than others, vehicles will become unbalanced, accumulating at some stations and becoming depleted at others. This problem is addressed by employing rebalancing drivers to drive vehicles from the popular destinations to the unpopular destinations. However, with this approach the rebalancing drivers themselves become unbalanced, and we need to “rebalance the rebalancers” by letting them travel back to the popular destinations with a customer. In this paper we study how to optimally route the rebalancing vehicles and drivers so that the number of waiting customers remains bounded while minimizing the number of rebalancing vehicles traveling in the network and the number of rebalancing drivers needed; surprisingly, these two objectives are aligned, and one can find the optimal rebalancing strategy by solving two decoupled linear programs. We determine the minimum number of drivers and minimum number of vehicles needed to ensure stability in the system. Our simulations suggest that, in Euclidean network topologies, one would need between 1/3 and 1/4 as many drivers as vehicles.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology. Laboratory for Information and Decision Systems
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DOI of Published Version
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6580187
