Modeling the preference of electric shared mobility drivers in choosing charging stations

被引:36
|
作者
Guo, Yuntao [1 ]
Qian, Xinwu [2 ]
Lei, Tian [3 ]
Guo, Shuocheng [2 ]
Gong, Lei [3 ]
机构
[1] Tongji Univ, Dept Traff Engn, Minist Educ, Key Lab Rd & Traff Engn, 4800 Caoan Rd, Shanghai 201804, Peoples R China
[2] Univ Alabama, Dept Civil Construct & Environm Engn, Tuscaloosa, AL 35487 USA
[3] Shenzhen Technol Univ, Coll Urban Transportat & Logist, Shenzhen 518118, Peoples R China
关键词
Electric taxi; Charging behavior; Driver habit; Quantile regression; CHOICE BEHAVIOR; QUANTILE REGRESSION; VEHICLE DRIVERS; LOCATION CHOICE; TAXI; TRAVEL; INFRASTRUCTURE; FEASIBILITY; INFORMATION; DEMAND;
D O I
10.1016/j.trd.2022.103399
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Electric vehicles for urban shared mobility services are important customers at public charging stations, and understanding their charging behavior is essential to the charging infrastructure planning and charging demand management. This study investigates the influencing factors on drivers' charging station selections for a large-scale and fully electrified taxi fleet with nearly 20,000 unique vehicles and over 35,000 drivers. Drivers' preference for a charging station is approximated by the driving time to it, and quantile regression models are estimated with explanatory variables on personal, charging station configuration, and built-environment factors. Study results suggest that whether a driver serves single- or double-shift, his or her charging habit, the charging station's accessibility to subway stations, and the charging duration are important factors affecting the choice outcomes. Insights derived in this study have important implications for planning charging infrastructure and managing charging demand for city-wide electric vehicles.
引用
收藏
页数:19
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