Details
Original language | English |
---|---|
Title of host publication | 32nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems |
Subtitle of host publication | ACM SIGSPATIAL 2024 |
Editors | Mario A. Nascimento, Li Xiong, Andreas Zufle, Yao-Yi Chiang, Ahmed Eldawy, Peer Kroger |
Pages | 410-420 |
Number of pages | 11 |
ISBN (electronic) | 9798400711077 |
Publication status | Published - 22 Nov 2024 |
Event | 32nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL 2024 - Atlanta, United States Duration: 29 Oct 2024 → 1 Nov 2024 |
Abstract
Battery electric vehicles (BEV) offer an opportunity to decrease transportation and mobility emissions significantly. The availability of charging station networks and infrastructure is crucial for the proliferation of BEVs. While the expansion of the charging networks is still slow, optimal utilization of the existing infrastructure and dispatching of mobile charging stations can serve as a bypass while more charging stations are built. In this work, we propose a novel multi-agent reinforcement learning - AdapMCS - approach for optimizing the adaptive dispatching of mobile charging stations to maximize the number of served charging requests by a charging station operator while improving the customer experience. By combining graph neural networks with reinforcement learning our approach is able to adapt to dynamic spatio-temporal changes in the demand distribution, for example, during big events such as concerts or fairs. Furthermore, we conduct a thorough evaluation using a publicly available real-world dataset and simulation of dynamic demand distribution changes. The results show that our adaptive dispatching approach is able to deal with the demand shifts and achieve significant gains for both customers, in terms of reducing waiting and charging times, and operators, in terms of increasing their profit.
Keywords
- Graph Neural Networks, Mobile Charging Stations, Multi-Agent, Reinforcement Learning
ASJC Scopus subject areas
- Computer Science(all)
- Information Systems
- Earth and Planetary Sciences(all)
- Earth-Surface Processes
- Mathematics(all)
- Modelling and Simulation
- Computer Science(all)
- Computer Graphics and Computer-Aided Design
- Computer Science(all)
- Computer Science Applications
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32nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems: ACM SIGSPATIAL 2024. ed. / Mario A. Nascimento; Li Xiong; Andreas Zufle; Yao-Yi Chiang; Ahmed Eldawy; Peer Kroger. 2024. p. 410-420.
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Adaptive Dispatching of Mobile Charging Stations using Multi-Agent Graph Convolutional Cooperative-Competitive Reinforcement Learning
AU - Wonsak, Shimon
AU - Henke, Nils
AU - Al-Rifai, Mohammad
AU - Nolting, Michael
AU - Nejdl, Wolfgang
N1 - Publisher Copyright: © 2024 Copyright held by the owner/author(s).
PY - 2024/11/22
Y1 - 2024/11/22
N2 - Battery electric vehicles (BEV) offer an opportunity to decrease transportation and mobility emissions significantly. The availability of charging station networks and infrastructure is crucial for the proliferation of BEVs. While the expansion of the charging networks is still slow, optimal utilization of the existing infrastructure and dispatching of mobile charging stations can serve as a bypass while more charging stations are built. In this work, we propose a novel multi-agent reinforcement learning - AdapMCS - approach for optimizing the adaptive dispatching of mobile charging stations to maximize the number of served charging requests by a charging station operator while improving the customer experience. By combining graph neural networks with reinforcement learning our approach is able to adapt to dynamic spatio-temporal changes in the demand distribution, for example, during big events such as concerts or fairs. Furthermore, we conduct a thorough evaluation using a publicly available real-world dataset and simulation of dynamic demand distribution changes. The results show that our adaptive dispatching approach is able to deal with the demand shifts and achieve significant gains for both customers, in terms of reducing waiting and charging times, and operators, in terms of increasing their profit.
AB - Battery electric vehicles (BEV) offer an opportunity to decrease transportation and mobility emissions significantly. The availability of charging station networks and infrastructure is crucial for the proliferation of BEVs. While the expansion of the charging networks is still slow, optimal utilization of the existing infrastructure and dispatching of mobile charging stations can serve as a bypass while more charging stations are built. In this work, we propose a novel multi-agent reinforcement learning - AdapMCS - approach for optimizing the adaptive dispatching of mobile charging stations to maximize the number of served charging requests by a charging station operator while improving the customer experience. By combining graph neural networks with reinforcement learning our approach is able to adapt to dynamic spatio-temporal changes in the demand distribution, for example, during big events such as concerts or fairs. Furthermore, we conduct a thorough evaluation using a publicly available real-world dataset and simulation of dynamic demand distribution changes. The results show that our adaptive dispatching approach is able to deal with the demand shifts and achieve significant gains for both customers, in terms of reducing waiting and charging times, and operators, in terms of increasing their profit.
KW - Graph Neural Networks
KW - Mobile Charging Stations
KW - Multi-Agent
KW - Reinforcement Learning
UR - http://www.scopus.com/inward/record.url?scp=85215088925&partnerID=8YFLogxK
U2 - 10.1145/3678717.3691262
DO - 10.1145/3678717.3691262
M3 - Conference contribution
AN - SCOPUS:85215088925
SP - 410
EP - 420
BT - 32nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
A2 - Nascimento, Mario A.
A2 - Xiong, Li
A2 - Zufle, Andreas
A2 - Chiang, Yao-Yi
A2 - Eldawy, Ahmed
A2 - Kroger, Peer
T2 - 32nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL 2024
Y2 - 29 October 2024 through 1 November 2024
ER -