Examining spatial disparities in electric vehicle charging station placements using machine learning
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DOI: 10.31219/osf.io/hvw2t
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- Peng, Ruoqing & Tang, Justin Hayse Chiwing G. & Yang, Xiong & Meng, Meng & Zhang, Jie & Zhuge, Chengxiang, 2024. "Investigating the factors influencing the electric vehicle market share: A comparative study of the European Union and United States," Applied Energy, Elsevier, vol. 355(C).
- Loni, Abdolah & Asadi, Somayeh, 2023. "Data-driven equitable placement for electric vehicle charging stations: Case study San Francisco," Energy, Elsevier, vol. 282(C).
- Ahmadian, Amirhossein & Ghodrati, Vahid & Gadh, Rajit, 2023. "Artificial deep neural network enables one-size-fits-all electric vehicle user behavior prediction framework," Applied Energy, Elsevier, vol. 352(C).
- Yuchao Cai & Jie Zhang & Quan Gu & Chenlu Wang, 2024. "An Analytical Framework for Assessing Equity of Access to Public Electric Vehicle Charging Stations: The Case of Shanghai," Sustainability, MDPI, vol. 16(14), pages 1-38, July.
- Spiller, Beia & Wilwerding, Rachel & Russo, Suzanne, 2024. "Equity in Electric Vehicle Charging Infrastructure," RFF Working Paper Series 24-14, Resources for the Future.
- Clark, Callie & Ozturk, Ayse Tugba & Hong, Preston & Gonzalez, Marta C. PhD & Moura, Scott J. PhD, 2022. "EEZ Mobility: A Toolf or Modeling Equitable Installation of Electric Vehicle Charging Stations," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt3jb779n1, Institute of Transportation Studies, UC Berkeley.
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NEP fields
This paper has been announced in the following NEP Reports:- NEP-BIG-2022-04-18 (Big Data)
- NEP-CMP-2022-04-18 (Computational Economics)
- NEP-ENE-2022-04-18 (Energy Economics)
- NEP-TRE-2022-04-18 (Transport Economics)
- NEP-URE-2022-04-18 (Urban and Real Estate Economics)
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