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, pick-up vehicles, and drive (or are driven) to their destination station wher...
| Main Authors: | , , |
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| Other Authors: | |
| Format: | Article |
| Language: | en_US |
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MIT Press
2013
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| Online Access: | http://hdl.handle.net/1721.1/81869 https://orcid.org/0000-0002-8293-0492 |
| _version_ | 1826218028055920640 |
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| author | Durrant-Whyte, H. Roy, Nicholas Abbeel, P. |
| author2 | Massachusetts Institute of Technology. Department of Aeronautics and Astronautics |
| author_facet | Massachusetts Institute of Technology. Department of Aeronautics and Astronautics Durrant-Whyte, H. Roy, Nicholas Abbeel, P. |
| author_sort | Durrant-Whyte, H. |
| 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, pick-up 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. We develop a rebalancing policy that minimizes the number of vehicles performing rebalancing trips. To do this, we utilize a fluid model for the customers and vehicles in the system. The model takes the form of a set of nonlinear time-delay differential equations. We then show that the optimal rebalancing policy can be found as the solution to a linear program. By analyzing the dynamical system model, we show that every station reaches an equilibrium in which there are excess vehicles and no waiting customers.We use this solution to develop a real-time rebalancing policy which can operate in highly variable environments. We verify policy performance in a simulated mobility-on-demand environment with stochastic features found in real-world urban transportation networks. |
| first_indexed | 2024-09-23T17:12:40Z |
| format | Article |
| id | mit-1721.1/81869 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T17:12:40Z |
| publishDate | 2013 |
| publisher | MIT Press |
| record_format | dspace |
| spelling | mit-1721.1/818692022-09-30T00:27:37Z Load Balancing for Mobility-on-Demand Systems Durrant-Whyte, H. Roy, Nicholas Abbeel, P. Massachusetts Institute of Technology. Department of Aeronautics and Astronautics Durrant-Whyte, H. Roy, Nicholas Abbeel, P. 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, pick-up 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. We develop a rebalancing policy that minimizes the number of vehicles performing rebalancing trips. To do this, we utilize a fluid model for the customers and vehicles in the system. The model takes the form of a set of nonlinear time-delay differential equations. We then show that the optimal rebalancing policy can be found as the solution to a linear program. By analyzing the dynamical system model, we show that every station reaches an equilibrium in which there are excess vehicles and no waiting customers.We use this solution to develop a real-time rebalancing policy which can operate in highly variable environments. We verify policy performance in a simulated mobility-on-demand environment with stochastic features found in real-world urban transportation networks. 2013-10-30T14:13:15Z 2013-10-30T14:13:15Z 2012 Article http://purl.org/eprint/type/JournalArticle 9780262305969 Paper-ID #184 http://hdl.handle.net/1721.1/81869 Durrant-Whyte, H., N. Roy, and P. Abbeel. "Load Balancing for Mobility-on-Demand Systems ." In Robotics: Science and Systems VII , Cambridge, MA: MIT Press, 2012. pp. 249 - 256. https://orcid.org/0000-0002-8293-0492 en_US http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6301044& Robotics: Science and Systems VII Creative Commons Attribution-Noncommercial-Share Alike 3.0 http://creativecommons.org/licenses/by-nc-sa/3.0/ application/pdf MIT Press Other univ. web domain |
| spellingShingle | Durrant-Whyte, H. Roy, Nicholas Abbeel, P. Load Balancing for Mobility-on-Demand Systems |
| title | Load Balancing for Mobility-on-Demand Systems |
| title_full | Load Balancing for Mobility-on-Demand Systems |
| title_fullStr | Load Balancing for Mobility-on-Demand Systems |
| title_full_unstemmed | Load Balancing for Mobility-on-Demand Systems |
| title_short | Load Balancing for Mobility-on-Demand Systems |
| title_sort | load balancing for mobility on demand systems |
| url | http://hdl.handle.net/1721.1/81869 https://orcid.org/0000-0002-8293-0492 |
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