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.

电动踏板车（e-scooter）共享系统（ESS）已被世界上许多城市广泛采用，并吸引了越来越多的用户。尽管一些研究以一个城市为例探讨了建筑环境的使用特点和对ESS乘车的影响，但很少有研究考虑过多个城市以获得可概括且可靠的结果。为了填补这一研究空白，我们收集了美国五个城市，即奥斯丁，明尼阿波利斯，堪萨斯城，路易斯维尔和波特兰的ESS旅行数据，并在控制了社会经济因素之后，研究了建筑环境对ESS乘车率的影响。不同城市的电动滑板车客运量的时间分布相似，在工作日和周末的11:30至17:30之间只有一个高峰期。在空间分布方面，与其他地区相比，大学和城市中心的ESS乘坐率更高。多级负二项式模型结果表明，人口普查区组中ESS出行与人口密度，就业密度，交叉路口密度，土地利用混合熵和公交车站密度成正相关。电动踏板车的人口数量与人口普查区组中位数人口年龄以及距市中心的距离呈负相关。本文中的发现可以帮助运营商了解影响共享电动踏板车的乘车率的因素，确定建筑环境变化时乘车率的变化，以及在新城市中实施ESS时确定高乘车率区域。