Application of Clustering Algorithms in the Location of Electric Taxi Charging Stations

Qing Li, Xue Li, Zuyu Liu and Yaping Qi
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Qing Li: College of Mathematics and System, Shandong University of Science and Technology, Qingdao 266590, China
Xue Li: College of Mathematics and System, Shandong University of Science and Technology, Qingdao 266590, China
Zuyu Liu: College of Mathematics and System, Shandong University of Science and Technology, Qingdao 266590, China
Yaping Qi: College of Electronic and Information Engineering, Shandong University of Science and Technology, Qingdao 266590, China

Sustainability, 2022, vol. 14, issue 13, 1-15

Abstract: The reasonable layout of charging stations is an important measure to improve the penetration rate of the electric taxi market. Based on the multi-type clustering algorithm, a widely applicable electric taxi charging stations locating method is proposed. By analyzing the massive gasoline taxi GPS trajectory data, the parking information and charging requirements of electric taxis are extracted, and the research area is divided into reasonable grids. Then, the divided grids are respectively subjected to multiple same-type clustering and multiple multi-type clustering algorithms, so as to help find out the location of the charging station, and a comparative analysis is performed. The empirical analysis shows that the positioning results of the multiple multi-type clustering algorithms are more reasonable than the multiple same-type clustering algorithms, which can effectively prolong the driving distance of electric taxis and save the travel time of drivers.

Keywords: charging station location; multiple and multi-type clustering; electric vehicle; GPS trajectory data (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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