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Deployment optimization for shared e-mobility systems with multi-agent deep neural search
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Luo, Man, Du, Bowen, Klemmer, Konstantin, Zhu, Hongming and Wen, Hongkai (2022) Deployment optimization for shared e-mobility systems with multi-agent deep neural search. IEEE Transactions on Intelligent Transportation Systems, 23 (3). pp. 2549-2560. doi:10.1109/TITS.2021.3125745 ISSN 1524-9050.
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Official URL: https://doi.org/10.1109/TITS.2021.3125745
Abstract
Shared e-mobility services have been widely tested and piloted in cities across the globe, and already woven into the fabric of modern urban planning. This paper studies a practical yet important problem in those systems: how to deploy and manage their infrastructure across space and time, so that the services are ubiquitous to the users while sustainable in profitability. However, in real-world systems evaluating the performance of different deployment strategies and then finding the optimal plan is prohibitively expensive, as it is often infeasible to conduct many iterations of trial-and-error. We tackle this by designing a high-fidelity simulation environment, which abstracts the key operation details of the shared e-mobility systems at fine-granularity, and is calibrated using data collected from the real-world. This allows us to try out arbitrary deployment plans to learn the optimal given specific context, before actually implementing any in the real-world systems. In particular, we propose a novel multi-agent neural search approach, in which we design a hierarchical controller to produce tentative deployment plans. The generated deployment plans are then tested using a multi-simulation paradigm, i.e., evaluated in parallel, where the results are used to train the controller with deep reinforcement learning. With this closed loop, the controller can be steered to have higher probability of generating better deployment plans in future iterations. The proposed approach has been evaluated extensively in our simulation environment, and experimental results show that it outperforms baselines e.g., human knowledge, and state-of-the-art heuristic-based optimization approaches in both service coverage and net revenue.
| Item Type: | Journal Article | |||||||||
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| Subjects: | H Social Sciences > HE Transportation and Communications Q Science > Q Science (General) Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software T Technology > TE Highway engineering. Roads and pavements 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): | Shared services (Management), Urban transportation -- Simulation methods, Electric vehicles, Reinforcement learning , Neural networks (Computer science) , Artificial intelligence -- Planning, Planning -- Computer simulation, Intelligent transportation systems | |||||||||
| Journal or Publication Title: | IEEE Transactions on Intelligent Transportation Systems | |||||||||
| Publisher: | IEEE | |||||||||
| ISSN: | 1524-9050 | |||||||||
| Official Date: | March 2022 | |||||||||
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| Volume: | 23 | |||||||||
| Number: | 3 | |||||||||
| Page Range: | pp. 2549-2560 | |||||||||
| DOI: | 10.1109/TITS.2021.3125745 | |||||||||
| Status: | Peer Reviewed | |||||||||
| Publication Status: | Published | |||||||||
| Reuse Statement (publisher, data, author rights): | © 2021 IEEE. 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: | 4 November 2021 | |||||||||
| Date of first compliant Open Access: | 8 November 2021 | |||||||||
| RIOXX Funder/Project Grant: |
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