research-article

Social-Aware Optimal Electric Vehicle Charger Deployment on Road Network

Authors:

Xiuwen ZhengAuthors Info & Claims

SIGSPATIAL '19: Proceedings of the 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems

Pages 398 - 407

https://doi.org/10.1145/3347146.3359382

Published: 05 November 2019 Publication History

Abstract

With the increasing awareness towards protecting environment, people are paying more attention to the electric vehicles (EVs). Accompanying the rapid growing number of EVs, challenges raise at the same time about how to place EV chargers (EVC), within a city, to satisfy multiple types of charging demand. To provide a better EVC station deployment plan to benefit the whole society, we propose a problem called Social-Aware Optimal Electric Vehicle Charger Deployment (SOCD) on road network. The SOCD problem is hard and different from existing work in three aspects, 1) we assume that the charging demand should be satisfied not only in urban areas but also in relatively rural areas; 2) our work is the first one that considers an EVC station should have multiple types of charging plugs, which is more reasonable in real world; 3) different from the regional deployment solutions in previous literature, our SOCD directly works on a real road network and EVC stations are placed at appropriate POIs laying on the road network. We show that the SOCD problem is NP-hard. To deal with the hardness, we design two heuristic algorithms whose efficiency and effectiveness can be experimentally demonstrated. Furthermore, we investigate the incremental case, that is, given an existing EVC station deployment plan and extra more budget, we need to decide where and how many to place more chargers. Finally, we conduct extensive experiments on real road network of Shanghai to demonstrate both effectiveness and efficiency of our algorithms.

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Wang YZeng YLi SZhang YZhou ZTong Y(2024)Efficient and Private Federated Trajectory MatchingIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.342441136:12(8079-8092)Online publication date: Dec-2024
https://doi.org/10.1109/TKDE.2024.3424411
Buchatskiy PTeploukhov SOnishchenko SLisov D(2024)Program Module for Locating Charging Stations for Electric Light Vehicles in a Settlement2024 International Conference on Information Processes and Systems Development and Quality Assurance (IPS)10.1109/IPS62349.2024.10499566(12-16)Online publication date: 19-Mar-2024
https://doi.org/10.1109/IPS62349.2024.10499566
Fan XGao YZhang F(2024)DST: Personalized Charging Station Recommendation for Electric Vehicles Based on Deep Reinforcement Learning and Spatio-Temporal Preference Analysis2024 IEEE International Conference on Web Services (ICWS)10.1109/ICWS62655.2024.00055(330-341)Online publication date: 7-Jul-2024
https://doi.org/10.1109/ICWS62655.2024.00055
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Index Terms

Social-Aware Optimal Electric Vehicle Charger Deployment on Road Network
1. Applied computing
  1. Operations research
    1. Transportation
2. Information systems
  1. Information systems applications
    1. Spatial-temporal systems
      1. Geographic information systems

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cover image ACM Conferences

SIGSPATIAL '19: Proceedings of the 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems

November 2019

648 pages

ISBN:9781450369091

DOI:10.1145/3347146

Copyright © 2019 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGSPATIAL: ACM Special Interest Group on Spatial Information

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Association for Computing Machinery

New York, NY, United States

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Published: 05 November 2019

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SIGSPATIAL '19

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SIGSPATIAL

SIGSPATIAL '19: 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems

November 5 - 8, 2019

IL, Chicago, USA

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SIGSPATIAL '19 Paper Acceptance Rate 34 of 161 submissions, 21%;

Overall Acceptance Rate 257 of 1,238 submissions, 21%

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Cited By

Wang YZeng YLi SZhang YZhou ZTong Y(2024)Efficient and Private Federated Trajectory MatchingIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.342441136:12(8079-8092)Online publication date: Dec-2024
https://doi.org/10.1109/TKDE.2024.3424411
Buchatskiy PTeploukhov SOnishchenko SLisov D(2024)Program Module for Locating Charging Stations for Electric Light Vehicles in a Settlement2024 International Conference on Information Processes and Systems Development and Quality Assurance (IPS)10.1109/IPS62349.2024.10499566(12-16)Online publication date: 19-Mar-2024
https://doi.org/10.1109/IPS62349.2024.10499566
Fan XGao YZhang F(2024)DST: Personalized Charging Station Recommendation for Electric Vehicles Based on Deep Reinforcement Learning and Spatio-Temporal Preference Analysis2024 IEEE International Conference on Web Services (ICWS)10.1109/ICWS62655.2024.00055(330-341)Online publication date: 7-Jul-2024
https://doi.org/10.1109/ICWS62655.2024.00055
Ting LLin CLin SChu YChuang K(2024)Multi-agent Reinforcement Learning for Online Placement of Mobile EV Charging StationsAdvances in Knowledge Discovery and Data Mining10.1007/978-981-97-2262-4_23(284-296)Online publication date: 25-Apr-2024
https://doi.org/10.1007/978-981-97-2262-4_23
Liu JSun JQi X(2023)Optimal Placement of Charging Stations in Road Networks: A Reinforcement Learning Approach with Attention MechanismApplied Sciences10.3390/app1314847313:14(8473)Online publication date: 22-Jul-2023
https://doi.org/10.3390/app13148473
Wang YZhao DRen YZhang DMa H(2023)SPAP: Simultaneous Demand Prediction and Planning for Electric Vehicle Chargers in a New CityACM Transactions on Knowledge Discovery from Data10.1145/356557717:4(1-25)Online publication date: 24-Feb-2023
https://dl.acm.org/doi/10.1145/3565577
von Wahl LTempelmeier NSao ADemidova EZhang ARangwala H(2022)Reinforcement Learning-based Placement of Charging Stations in Urban Road NetworksProceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3534678.3539154(3992-4000)Online publication date: 14-Aug-2022
https://dl.acm.org/doi/10.1145/3534678.3539154
Zhang WLiu HXiong HXu TWang FXin HWu H(2022)RLCharge: Imitative Multi-Agent Spatiotemporal Reinforcement Learning for Electric Vehicle Charging Station RecommendationIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2022.3178819(1-1)Online publication date: 2022
https://doi.org/10.1109/TKDE.2022.3178819
Luo MDu BKlemmer KZhu HWen H(2022)Deployment Optimization for Shared e-Mobility Systems With Multi-Agent Deep Neural SearchIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2021.312574523:3(2549-2560)Online publication date: Mar-2022
https://doi.org/10.1109/TITS.2021.3125745

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