@inproceedings{d57f9c1c89f34cb388bad35f7b033055,
title = "Data driven charging station placement",
abstract = "With the rapid increasing availability of EV (electric vehicle) users, the demand for charging stations has also become vast. In the meanwhile, where to place the stations and what factors have major influence, remains unclear. These problems are bothering when EV companies tries to decide the locations for charging stations. Therefore, we tried to find an effective and interpretable approach to place them in more efficient locations. In common sense, a better location to place a station should relatively has a higher usage rate. Intuitively, we decided to predict usage rates of the candidate locations and tried to explain the result in the meantime, i.e. to find out how much important each feature is or what kind of influence they have. In this paper, we implement 2 models for the usage rate prediction. We also conduced experiments on real datasets, which contains the real charging records of anyo charging company in Shanghai. Further analysis is conducted as well for interpretation of the experiment result, including feature importance.",
keywords = "Charging station, Feature importance, Location selection",
author = "Yudi Guo and Junjie Yao and Jiaxiang Huang and Yijun Chen",
note = "Publisher Copyright: {\textcopyright} 2019, Springer Nature Switzerland AG.; 3rd APWeb and WAIM Joint Conference on Web and Big Data, APWeb-WAIM 2019 ; Conference date: 01-08-2019 Through 03-08-2019",
year = "2019",
doi = "10.1007/978-3-030-26075-0\_20",
language = "英语",
isbn = "9783030260743",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "260--267",
editor = "Jie Shao and Yiu, \{Man Lung\} and Masashi Toyoda and Dongxiang Zhang and Wei Wang and Bin Cui",
booktitle = "Web and Big Data - 3rd International Joint Conference, APWeb-WAIM 2019, Proceedings",
address = "德国",
}