Shared e-scooters have become a common mode of transportation in many cities around the world. E-sooters provide convenient and quick rides for short distances and can act as a connection for first/last mile trips. To date, limited studies have explored the spatial variation of e-scooter trips and there is knowledge to be gained by investigating variables associated with e-scooter trip generation. This study implemented a spatial analysis approach, Geographical Weighted Regression (GWR), to explore how factors relating to demographics, density, diversity, design, urbanism scores, distance to transit and other transportation-related variables influence e-scooter trips in Louisville, KY. More than 400,000 e-scooter trips across 159 Traffic Analysis Zones (TAZs) were included in the study. Results show TAZ-level factors including land use, age distribution, gender distribution, Walk Score and Park Score impacted the density of e-scooters trips in the TAZ. The GWR model showed improvements over a global Ordinary Least Squares (OLS) model. Local goodness of fit ranged from 0.732–0.895 across the study area. This study can help governments and e-scooter sharing companies develop policies that maximize e-scooter use, equity, and accessibility while improving the mobility of cities.

共享电动代步车已成为世界上许多城市的一种常见交通方式。E-轻便摩托车可以在短距离内提供便捷的乘车服务，并且可以作为第一英里/最后一英里旅行的连接点。迄今为止，有限的研究已经探索了电动踏板车行程的空间变化，并且通过研究与电动踏板车行程相关的变量可以获得知识。这项研究实施了一种空间分析方法，即地理加权回归（GWR），以探索与人口统计学，密度，多样性，设计，城市化得分，过境距离和其他运输相关变量相关的因素如何影响肯塔基州路易斯维尔的电动踏板车旅行。该研究包括横跨159个交通分析区（TAZ）的400,000多辆电动踏板车旅行。结果显示了TAZ级别的因素，包括土地使用，年龄分布，性别分布，步行分数和公园分数影响了TAZ中电动滑板车出行的密度。GWR模型显示了对全球普通最小二乘（OLS）模型的改进。在整个研究区域中，局部拟合优度在0.732–0.895之间。这项研究可以帮助政府和电动踏板车共享公司制定政策，以最大程度地利用电动踏板车，提高公平性和可及性，同时改善城市的流动性。