Open AccessOptimal model of locating charging stations with massive urban trajectories
Author(s) -
Danhui Huang,
Yuan Chen,
Xiaochen Pan
Publication year - 2020
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/715/1/012009
Subject(s) - key (lock) , reliability (semiconductor) , cluster analysis , computer science , electric vehicle , track (disk drive) , rationality , scale (ratio) , power (physics) , charging station , radius , government (linguistics) , cluster (spacecraft) , transport engineering , operations research , engineering , computer security , computer network , artificial intelligence , linguistics , physics , philosophy , quantum mechanics , political science , law , operating system
Popularization of electric vehicles (EVs) is a promising approach towards realizing environmentally friendly transportation. While government has implemented advantageous policy efforts to boost EVs, how to effectively build electric charging stations becomes a key link. A reasonable distribution should not only conform to user habits and potential behaviour patterns, but also satisfying the charging reliability. In this paper, vehicle track data and POI data are used to cluster with road network information. A clustering algorithm based on R serving radius is proposed to improve the results and make them accommodate the rationality of the real world. Finally, suggestions are made for each charging station power supply scale according to regional difference. Experiments show that the proposed model has practical guiding significance.