Optimizing Profitability of E-Scooter Sharing System via Battery-Aware Recommendation

Jiwon Kim, Taewoong Jung, Yonghun Choi, Daeyong Kim, Hojung Cha

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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 languageEnglish
Title of host publicationMOBISYS 2024 - Proceedings of the 2024 22nd Annual International Conference on Mobile Systems, Applications and Services
PublisherAssociation for Computing Machinery, Inc
Pages575-587
Number of pages13
ISBN (Electronic)9798400705816
DOIs
Publication statusPublished - 2024 Jun 3
Event22nd Annual International Conference on Mobile Systems, Applications and Services, MOBISYS 2024 - Minato-ku, Japan
Duration: 2024 Jun 32024 Jun 7

Publication series

NameMOBISYS 2024 - Proceedings of the 2024 22nd Annual International Conference on Mobile Systems, Applications and Services

Conference

Conference22nd Annual International Conference on Mobile Systems, Applications and Services, MOBISYS 2024
Country/TerritoryJapan
CityMinato-ku
Period24/6/324/6/7

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

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