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Fleet rebalancing for expanding shared e-mobility systems : a multi-agent deep reinforcement learning approach
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Luo, Man, Du, Bowen, Zhang, Wenzhe, Song, Tianyou, Liu, Kun, Zhu, Hongming, Birkin, Mark and Wen, Hongkai (2022) Fleet rebalancing for expanding shared e-mobility systems : a multi-agent deep reinforcement learning approach. IEEE Transactions on Intelligent Transportation Systems . ISSN 1524-9050. (In Press)
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Abstract
The electrification of shared mobility has become popular across the globe. Many cities have their new shared e-mobility systems deployed, with continuously expanding coverage from central areas to the city edges. A key challenge in the operation of these systems is fleet rebalancing, i.e., how EVs should be repositioned to better satisfy future demand. This is particularly challenging in the context of expanding systems, because i) the range of the EVs is limited while charging time is typically long, which constrain the viable rebalancing operations; and ii) the EV stations in the system are dynamically changing, i.e., the legitimate targets for rebalancing operations can vary over time. We tackle these challenges by first investigating rich sets of data collected from a real-world shared e-mobility system for one year, analyzing the operation model, usage patterns and expansion dynamics of this new mobility mode. With the learned knowledge we design a high-fidelity simulator, which is able to abstract key operation details of EV sharing at fine granularity. Then we model the rebalancing task for shared e-mobility systems under continuous expansion as a Multi-Agent Reinforcement Learning (MARL) problem, which directly takes the range and charging properties of the EVs into account. We further propose a novel policy optimization approach with action cascading, which is able to cope with the expansion dynamics and solve the formulated MARL. We evaluate the proposed approach extensively, and experimental results show that our approach outperforms the state-of-the-art, offering significant performance gain in both satisfied demand and net revenue.
| Item Type: | Journal Article | ||||||
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| Subjects: | Q Science > Q Science (General) Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software T Technology > TA Engineering (General). Civil engineering (General) T Technology > TL Motor vehicles. Aeronautics. Astronautics |
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| Divisions: | Faculty of Science, Engineering and Medicine > Science > Computer Science | ||||||
| Library of Congress Subject Headings (LCSH): | Electric vehicles , Reinforcement learning, Urban transportation -- Data processing, Neural networks (Computer science) | ||||||
| Journal or Publication Title: | IEEE Transactions on Intelligent Transportation Systems | ||||||
| Publisher: | IEEE | ||||||
| ISSN: | 1524-9050 | ||||||
| Official Date: | 2022 | ||||||
| Dates: |
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| Status: | Peer Reviewed | ||||||
| Publication Status: | In Press | ||||||
| Reuse Statement (publisher, data, author rights): | © 2022 Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | ||||||
| Access rights to Published version: | Restricted or Subscription Access | ||||||
| Date of first compliant deposit: | 16 December 2022 | ||||||
| Date of first compliant Open Access: | 16 December 2022 | ||||||
| RIOXX Funder/Project Grant: |
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