IDEAS home Printed from https://ideas.repec.org/a/eee/jotrge/v74y2019icp173-180.html
   My bibliography  Save this article

Urban public charging station locating method for electric vehicles based on land use approach

Author

Listed:
  • Csiszár, Csaba
  • Csonka, Bálint
  • Földes, Dávid
  • Wirth, Ervin
  • Lovas, Tamás

Abstract

Since the use of electric vehicles decreases the local pollution and noise emission electromobility gains a huge potential in sustainable transport. The currently insufficient charging network, namely the low number of stations and ill-chosen locations are a significant barrier of the widespread of electric vehicles in many countries. Accordingly, localization of new charging stations is of utmost importance. In this study, we develop a two-level charging station locating method. Weighted multicriteria methods were introduced to evaluate territory segments and allocate charging stations within a segment applying a hexagon-based approach and using a greedy algorithm. The novelty of the method in comparison with previous studies is that it assesses the potential of electric vehicle use on macro-level and the possible locations of charging stations on micro-level with a focus on the land-use. We apply the method to Hungary (macro evaluation) and to a district of the capital city, Budapest (micro evaluation). It is found that charging stations at P + R facilities, close to concentrated services and high-density areas are better suited to serve urban public charging demand than gas stations.

Suggested Citation

  • Csiszár, Csaba & Csonka, Bálint & Földes, Dávid & Wirth, Ervin & Lovas, Tamás, 2019. "Urban public charging station locating method for electric vehicles based on land use approach," Journal of Transport Geography, Elsevier, vol. 74(C), pages 173-180.
  • Handle: RePEc:eee:jotrge:v:74:y:2019:i:c:p:173-180
    DOI: 10.1016/j.jtrangeo.2018.11.016
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S096669231830471X
    Download Restriction: no

    File URL: https://libkey.io/10.1016/j.jtrangeo.2018.11.016?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Awasthi, Abhishek & Venkitusamy, Karthikeyan & Padmanaban, Sanjeevikumar & Selvamuthukumaran, Rajasekar & Blaabjerg, Frede & Singh, Asheesh K., 2017. "Optimal planning of electric vehicle charging station at the distribution system using hybrid optimization algorithm," Energy, Elsevier, vol. 133(C), pages 70-78.
    2. Zhu, Zhi-Hong & Gao, Zi-You & Zheng, Jian-Feng & Du, Hao-Ming, 2016. "Charging station location problem of plug-in electric vehicles," Journal of Transport Geography, Elsevier, vol. 52(C), pages 11-22.
    3. Upchurch, Christopher & Kuby, Michael, 2010. "Comparing the p-median and flow-refueling models for locating alternative-fuel stations," Journal of Transport Geography, Elsevier, vol. 18(6), pages 750-758.
    4. Alegre, Susana & Míguez, Juan V. & Carpio, José, 2017. "Modelling of electric and parallel-hybrid electric vehicle using Matlab/Simulink environment and planning of charging stations through a geographic information system and genetic algorithms," Renewable and Sustainable Energy Reviews, Elsevier, vol. 74(C), pages 1020-1027.
    5. Erbaş, Mehmet & Kabak, Mehmet & Özceylan, Eren & Çetinkaya, Cihan, 2018. "Optimal siting of electric vehicle charging stations: A GIS-based fuzzy Multi-Criteria Decision Analysis," Energy, Elsevier, vol. 163(C), pages 1017-1031.
    6. Arias, Mariz B. & Bae, Sungwoo, 2016. "Electric vehicle charging demand forecasting model based on big data technologies," Applied Energy, Elsevier, vol. 183(C), pages 327-339.
    7. Andrenacci, N. & Ragona, R. & Valenti, G., 2016. "A demand-side approach to the optimal deployment of electric vehicle charging stations in metropolitan areas," Applied Energy, Elsevier, vol. 182(C), pages 39-46.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Pichamon Keawthong & Veera Muangsin & Chupun Gowanit, 2022. "Location Selection of Charging Stations for Electric Taxis: A Bangkok Case," Sustainability, MDPI, vol. 14(17), pages 1-23, September.
    2. Wang, Yue & Shi, Jianmai & Wang, Rui & Liu, Zhong & Wang, Ling, 2018. "Siting and sizing of fast charging stations in highway network with budget constraint," Applied Energy, Elsevier, vol. 228(C), pages 1255-1271.
    3. Lin, Haiyang & Bian, Caiyun & Wang, Yu & Li, Hailong & Sun, Qie & Wallin, Fredrik, 2022. "Optimal planning of intra-city public charging stations," Energy, Elsevier, vol. 238(PC).
    4. Morro-Mello, Igoor & Padilha-Feltrin, Antonio & Melo, Joel D. & Calviño, Aida, 2019. "Fast charging stations placement methodology for electric taxis in urban zones," Energy, Elsevier, vol. 188(C).
    5. Du, Jiuyu & Ouyang, Danhua, 2017. "Progress of Chinese electric vehicles industrialization in 2015: A review," Applied Energy, Elsevier, vol. 188(C), pages 529-546.
    6. Zhang, Yue & Zhang, Qi & Farnoosh, Arash & Chen, Siyuan & Li, Yan, 2019. "GIS-Based Multi-Objective Particle Swarm Optimization of charging stations for electric vehicles," Energy, Elsevier, vol. 169(C), pages 844-853.
    7. Morro-Mello, Igoor & Padilha-Feltrin, Antonio & Melo, Joel D. & Heymann, Fabian, 2021. "Spatial connection cost minimization of EV fast charging stations in electric distribution networks using local search and graph theory," Energy, Elsevier, vol. 235(C).
    8. Csiszár, Csaba & Csonka, Bálint & Földes, Dávid & Wirth, Ervin & Lovas, Tamás, 2020. "Location optimisation method for fast-charging stations along national roads," Journal of Transport Geography, Elsevier, vol. 88(C).
    9. Zhou, Guangyou & Zhu, Zhiwei & Luo, Sumei, 2022. "Location optimization of electric vehicle charging stations: Based on cost model and genetic algorithm," Energy, Elsevier, vol. 247(C).
    10. Sanchari Deb & Kari Tammi & Karuna Kalita & Pinakeswar Mahanta, 2018. "Review of recent trends in charging infrastructure planning for electric vehicles," Wiley Interdisciplinary Reviews: Energy and Environment, Wiley Blackwell, vol. 7(6), November.
    11. Ko, Sungmin & Shin, Jungwoo, 2023. "Projection of fuel cell electric vehicle demand reflecting the feedback effects between market conditions and market share affected by spatial factors," Energy Policy, Elsevier, vol. 173(C).
    12. Zhi Wu & Yuxuan Zhuang & Suyang Zhou & Shuning Xu & Peng Yu & Jinqiao Du & Xiner Luo & Ghulam Abbas, 2020. "Bi-Level Planning of Multi-Functional Vehicle Charging Stations Considering Land Use Types," Energies, MDPI, vol. 13(5), pages 1-17, March.
    13. Yue Wang & Zhong Liu & Jianmai Shi & Guohua Wu & Rui Wang, 2018. "Joint Optimal Policy for Subsidy on Electric Vehicles and Infrastructure Construction in Highway Network," Energies, MDPI, vol. 11(9), pages 1-21, September.
    14. Ömer Kaya & Kadir Diler Alemdar & Tiziana Campisi & Ahmet Tortum & Merve Kayaci Çodur, 2021. "The Development of Decarbonisation Strategies: A Three-Step Methodology for the Suitable Analysis of Current EVCS Locations Applied to Istanbul, Turkey," Energies, MDPI, vol. 14(10), pages 1-21, May.
    15. Wang, Hua & Zhao, De & Cai, Yutong & Meng, Qiang & Ong, Ghim Ping, 2021. "Taxi trajectory data based fast-charging facility planning for urban electric taxi systems," Applied Energy, Elsevier, vol. 286(C).
    16. Micari, Salvatore & Polimeni, Antonio & Napoli, Giuseppe & Andaloro, Laura & Antonucci, Vincenzo, 2017. "Electric vehicle charging infrastructure planning in a road network," Renewable and Sustainable Energy Reviews, Elsevier, vol. 80(C), pages 98-108.
    17. Maria-Simona Răboacă & Irina Băncescu & Vasile Preda & Nicu Bizon, 2020. "An Optimization Model for the Temporary Locations of Mobile Charging Stations," Mathematics, MDPI, vol. 8(3), pages 1-20, March.
    18. Helmus, Jurjen R. & Lees, Michael H. & van den Hoed, Robert, 2022. "A validated agent-based model for stress testing charging infrastructure utilization," Transportation Research Part A: Policy and Practice, Elsevier, vol. 159(C), pages 237-262.
    19. Mu Li & Yingqi Liu & Weizhong Yue, 2022. "Evolutionary Game of Actors in China’s Electric Vehicle Charging Infrastructure Industry," Energies, MDPI, vol. 15(23), pages 1-20, November.
    20. Miao, Hongzhi & Jia, Hongfei & Li, Jiangchen & Qiu, Tony Z., 2019. "Autonomous connected electric vehicle (ACEV)-based car-sharing system modeling and optimal planning: A unified two-stage multi-objective optimization methodology," Energy, Elsevier, vol. 169(C), pages 797-818.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:jotrge:v:74:y:2019:i:c:p:173-180. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/journal-of-transport-geography .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.