Details
Originalsprache | Englisch |
---|---|
Titel des Sammelwerks | 32nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems |
Untertitel | ACM SIGSPATIAL 2024 |
Herausgeber/-innen | Mario A. Nascimento, Li Xiong, Andreas Zufle, Yao-Yi Chiang, Ahmed Eldawy, Peer Kroger |
Seiten | 410-420 |
Seitenumfang | 11 |
ISBN (elektronisch) | 9798400711077 |
Publikationsstatus | Veröffentlicht - 22 Nov. 2024 |
Veranstaltung | 32nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL 2024 - Atlanta, USA / Vereinigte Staaten Dauer: 29 Okt. 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.
ASJC Scopus Sachgebiete
- Informatik (insg.)
- Information systems
- Erdkunde und Planetologie (insg.)
- Erdoberflächenprozesse
- Mathematik (insg.)
- Modellierung und Simulation
- Informatik (insg.)
- Computergrafik und computergestütztes Design
- Informatik (insg.)
- Angewandte Informatik
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32nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems: ACM SIGSPATIAL 2024. Hrsg. / Mario A. Nascimento; Li Xiong; Andreas Zufle; Yao-Yi Chiang; Ahmed Eldawy; Peer Kroger. 2024. S. 410-420.
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › 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 -