NDLI: Rebalancing the rebalancers: optimally routing vehicles and drivers in mobility-on-demand systems

Content Provider	IEEE Xplore Digital Library
Author	Smith, S.L. Pavone, M. Schwager, M. Frazzoli, E. Rus, D.
Copyright Year	2013
Description	Author affiliation: Dept. of Aeronaut. & Astronaut., Stanford Univ., Stanford, CA, USA (Pavone, M.) \|\| Aeronaut. & Astronaut. Dept., Massachusetts Inst. of Technol., Cambridge, MA, USA (Frazzoli, E.) \|\| Dept. of Mech. Eng., Boston Univ., Boston, MA, USA (Schwager, M.) \|\| Electr. Eng. & Comput. Sci. Dept., Massachusetts Inst. of Technol., Cambridge, MA, USA (Rus, D.) \|\| Dept. of Electr. & Comput. Eng., Univ. of Waterloo, Waterloo, ON, Canada (Smith, S.L.)
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.
Starting Page	2362
Ending Page	2367
File Size	493672
Page Count	6
File Format	PDF
ISBN	9781479901777
ISSN	07431619
e-ISBN	9781479901784
DOI	10.1109/ACC.2013.6580187
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-06-17
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	American Automatic Control Council(AACC)
Subject Keyword	Vehicles Optimization Stability analysis Vehicle dynamics Mathematical model Asymptotic stability Routing
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering

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