E-scooter sharing is a potential feeder to complement public transit for alleviating the first-and-last-mile problem. This study investigates the integration between shared e-scooters and public transit by conducting a comparative analysis in 124 European cities based on vehicle availability data. Results suggest that the integration ratios of e-scooter sharing in different cities show significant variations and range from 5.59% to 51.40% with a mean value of 31.58% and a standard deviation of 8.47%. The temporal patterns of integration ratio for first- and last-mile trips present an opposite trend. An increase in the integration ratio for first-mile trips is related to a decrease in the integration ratio for last mile in the time series. Additionally, these cities can be divided into four clusters according to their temporal variations of the integration ratios by a bottom-up hierarchical clustering method. Meanwhile, we explore the nonlinear effects of city-level factors on the integration ratio using explainable machine learning. Several factors are found to have noticeable and nonlinear influences. For example, the density of public transit stations and a higher ratio of the young are positively associated with the integration ratio to a certain extent. The results potentially support transport planners to collectively optimize and manage e-scooter sharing and public transport to facilitate multi-modal transport systems.

电动滑板车共享是补充公共交通的潜在支线，可以缓解第一英里和最后一英里的问题。本研究根据车辆可用性数据对 124 个欧洲城市进行比较分析，探讨共享电动滑板车与公共交通的整合。结果表明，不同城市共享电动滑板车的整合率存在显着差异，范围为5.59%至51.40%，平均值为31.58%，标准差为8.47%。第一英里和最后一英里出行整合率的时间模式呈现相反的趋势。时间序列中第一英里行程积分率的增加与最后一英里积分率的下降相关。此外，通过自下而上的层次聚类方法，可以根据整合比率的时间变化将这些城市分为四个集群。同时，我们利用可解释的机器学习探索城市层面因素对整合率的非线性影响。发现有几个因素具有显着的非线性影响。例如，公交站点密度和年轻人比例较高，在一定程度上与融合率呈正相关。研究结果可能支持交通规划者共同优化和管理电动滑板车共享和公共交通，以促进多式联运系统。