Abstract
In e-scooter sharing systems, users randomly select and use e-scooters based on inaccurate battery information. This simple rental policy leads to low profitability on two fronts. First, inaccurate battery information causes unexpected device shutdowns, causing negative user experiences and refunds. Second, randomly selected e-scooters increase operation costs for battery management. In this paper, we propose e-scooter recommendation system, EcoRide, which provides accurate battery estimation and profitable e-scooter selection to maximize profitability of sharing systems. To this end, we propose a battery estimation considering four factors, i.e., battery state, temperature, user weight, and road slope, that affect the available battery energy in e-scooter applications. We define a parameter, dynamic voltage threshold (DVT), to represent dynamically changing battery energy, and use it to estimate battery availability. Next, to achieve cost-effective e-scooter selection, we introduce a multi-Agent reinforcement learning (MARL)-based technique to learn policies that minimize operation costs. We define sharing system operation as a MARL problem with an objective function based on battery management costs. To cope with unstable training due to a wide service area and multiple requests, a centralized training technique is adopted. The proposed battery estimation and e-scooter selection technique are validated through actual driving tests and a sharing system simulator, respectively. Additionally, our case study using open data from Washington D.C. demonstrates a profit gain of up to 68% with EcoRide.
Original language | English |
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Title of host publication | MOBISYS 2024 - Proceedings of the 2024 22nd Annual International Conference on Mobile Systems, Applications and Services |
Publisher | Association for Computing Machinery, Inc |
Pages | 575-587 |
Number of pages | 13 |
ISBN (Electronic) | 9798400705816 |
DOIs | |
Publication status | Published - 2024 Jun 3 |
Event | 22nd Annual International Conference on Mobile Systems, Applications and Services, MOBISYS 2024 - Minato-ku, Japan Duration: 2024 Jun 3 → 2024 Jun 7 |
Publication series
Name | MOBISYS 2024 - Proceedings of the 2024 22nd Annual International Conference on Mobile Systems, Applications and Services |
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Conference
Conference | 22nd Annual International Conference on Mobile Systems, Applications and Services, MOBISYS 2024 |
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Country/Territory | Japan |
City | Minato-ku |
Period | 24/6/3 → 24/6/7 |
Bibliographical note
Publisher Copyright:© 2024 Copyright held by the owner/author(s).
All Science Journal Classification (ASJC) codes
- Artificial Intelligence
- Computer Networks and Communications
- Information Systems
- Software
- Safety, Risk, Reliability and Quality
- Health Informatics
- Instrumentation
- Radiation