The DOI matches its Crossref record; 23 of the 23 references it cites were found in a registry or an index.
Robotic load balancing for mobility-on-demand systems
Open accessAbstract
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.
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Cites
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A survey on pickup and delivery problems
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Cited by
Planning and Decision-Making for Autonomous Vehicles
Schwarting, W., Alonso-Mora, J., & Rus, D. (2018) · Annual Review of Control, Robotics, and Autonomous Systems · doi:10.1146/annurev-control-060117-105157 · Cited by 972
Self-citationAssessing the Long-term Effects of Autonomous Vehicles: A Speculative Approach
Gruel, W., & Stanford, J. M. (2016) · Transportation Research Procedia · doi:10.1016/j.trpro.2016.05.003 · Cited by 166
Rossi, F., Zhang, R., Hindy, Y., & Pavone, M. (2018) · Autonomous Robots · doi:10.1007/s10514-018-9750-5 · Cited by 153
Self-citationCongestion-aware system optimal route choice for shared autonomous vehicles
Levin, M. W. (2017) · Transportation Research Part C: Emerging Technologies · doi:10.1016/j.trc.2017.06.020 · Cited by 152
Rebalancing shared mobility-on-demand systems: A reinforcement learning approach
Wen, J., Zhao, J., & Jaillet, P. (2017) · 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC) · doi:10.1109/itsc.2017.8317908 · Cited by 149
Metadata
- Authors
- Pavone, M., Smith, S. L., Frazzoli, E., & Rus, D.
- Venue
- The International Journal of Robotics Research
- Publisher
- SAGE Publications
- Published
- 2012-05-11
- Volume
- 31
- Issue
- 7
- Pages
- 839-854
- ISSN
- 0278-3649 (ISSN-L)
- 1741-3176
- OpenAlex
- W2064526960
- Cited by
- 287
- References
- 23
- FWCI
- 10.97
- Type
- journal article
- Language
- en
- Open access
- green
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
