Robotic load balancing for mobility-on-demand systems
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...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | en_US |
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Sage Publications
2013
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| Online Access: | http://hdl.handle.net/1721.1/81451 https://orcid.org/0000-0001-5473-3566 https://orcid.org/0000-0002-0505-1400 |
| _version_ | 1826207858011668480 |
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| author | Frazzoli, Emilio Rus, Daniela L. Pavone, Marco Smith, Stephen L. |
| author2 | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory |
| author_facet | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Frazzoli, Emilio Rus, Daniela L. Pavone, Marco Smith, Stephen L. |
| author_sort | Frazzoli, Emilio |
| collection | MIT |
| description | 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. |
| first_indexed | 2024-09-23T13:56:03Z |
| format | Article |
| id | mit-1721.1/81451 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T13:56:03Z |
| publishDate | 2013 |
| publisher | Sage Publications |
| record_format | dspace |
| spelling | mit-1721.1/814512022-09-28T17:09:34Z Robotic load balancing for mobility-on-demand systems Frazzoli, Emilio Rus, Daniela L. Pavone, Marco Smith, Stephen L. 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 Frazzoli, Emilio Rus, Daniela L. 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. Singapore-MIT Alliance for Research and Technology Center United States. Office of Naval Research (Grant N000140911051) National Science Foundation (U.S.) (Grant EFRI0735953) 2013-10-21T15:18:07Z 2013-10-21T15:18:07Z 2012-05 Article http://purl.org/eprint/type/JournalArticle 0278-3649 1741-3176 http://hdl.handle.net/1721.1/81451 Pavone, M., S. L. Smith, E. Frazzoli, and D. Rus. “Robotic load balancing for mobility-on-demand systems.” The International Journal of Robotics Research 31, no. 7 (May 30, 2012): 839-854. https://orcid.org/0000-0001-5473-3566 https://orcid.org/0000-0002-0505-1400 en_US http://dx.doi.org/10.1177/0278364912444766 The International Journal of Robotics Research Creative Commons Attribution-Noncommercial-Share Alike 3.0 http://creativecommons.org/licenses/by-nc-sa/3.0/ application/pdf Sage Publications |
| spellingShingle | Frazzoli, Emilio Rus, Daniela L. Pavone, Marco Smith, Stephen L. Robotic load balancing for mobility-on-demand systems |
| title | Robotic load balancing for mobility-on-demand systems |
| title_full | Robotic load balancing for mobility-on-demand systems |
| title_fullStr | Robotic load balancing for mobility-on-demand systems |
| title_full_unstemmed | Robotic load balancing for mobility-on-demand systems |
| title_short | Robotic load balancing for mobility-on-demand systems |
| title_sort | robotic load balancing for mobility on demand systems |
| url | http://hdl.handle.net/1721.1/81451 https://orcid.org/0000-0001-5473-3566 https://orcid.org/0000-0002-0505-1400 |
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