Influence of the built environment on E-scooter sharing ridership: A tale of five cities

被引:95
作者
Huo, Jinghai [1 ]
Yang, Hongtai [1 ]
Li, Chaojing [1 ]
Zheng, Rong [1 ]
Yang, Linchuan [2 ]
Wen, Yi [3 ]
机构
[1] Southwest Jiaotong Univ, Natl United Engn Lab Integrated & Intelligent Tra, Sch Transportat & Logist, Inst Syst Sci & Engn,Natl Engn Lab Integrated Tra, Chengdu, Peoples R China
[2] Southwest Jiaotong Univ, Dept Urban & Rural Planning, Chengdu, Peoples R China
[3] Univ Tennessee, Dept Civil & Environm Engn, 325 John D Tickle Bldg,851 Neyland Dr, Knoxville, TN 37996 USA
基金
中国国家自然科学基金; 国家教育部科学基金资助; 中国博士后科学基金;
关键词
Travel demand; Land use; Multilevel model; Spatiotemporal data; Micro mobility; Shared mobility; BIKE-SHARE; TRANSIT RIDERSHIP; USAGE PATTERNS; WASHINGTON; AUSTIN; TRAVEL;
D O I
10.1016/j.jtrangeo.2021.103084
中图分类号
F [经济];
学科分类号
02 ;
摘要
Electric scooter (e-scooter) sharing systems (ESSs) have been widely adopted by many cities around the world and have attracted a growing number of users. Although some studies have explored the usage characteristics and effects of the built environment on ESS ridership using one city as an example, few studies have considered multiple cities to obtain generalizable and robust results. To fill this research gap, we collect the ESS trip data of five cities in the U.S., namely Austin, Minneapolis, Kansas City, Louisville, and Portland, and explore the effects of the built environment on ESS ridership after controlling for socioeconomic factors. The temporal distributions of e-scooter ridership of different cities are similar, having a single peak period on weekdays and weekends between 11:30 and 17:30. In terms of spatial distribution, the ESS ridership is higher in universities and urban centers compared to other areas. Multilevel negative binomial model results show that ESS trips are positively correlated with population density, employment density, intersection density, land use mixed entropy, and bus stop density in the census block group. E-scooter ridership is negatively correlated with the median age of the population in the census block group and distance to the city center. The findings in this article can help operators understand the factors that affect the ridership of shared e-scooters, determine the changes in ridership when the built environment changes, and identify high-ridership areas when ESS is implemented in new cities.
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页数:9
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