Shared e-scooters are increasingly recognised as a sustainable travel mode for tackling urban transportation challenges, including congestion and air pollution. Their early adoption in U.S. cities, followed by expansion across Europe and ongoing trials in the UK, underscores the need to understand key factors for the scheme’s success and development, particularly regarding the acceptance and retention of existing users. This study adopts an explainable machine learning approach to predict and understand the likelihood of continued shared e-scooter usage among current users. It provides insights into the important factors influencing user retention, while also comparing the UK/EU and US contexts. The developed models exhibit high predictive accuracy and highlight the perceived utility and user-friendliness of e-scooters as key determinants influencing users’ willingness for future use. The research sheds light on psychographic characteristics, emphasising the impact of green awareness and individual’s values on users’ retention, and how their importance may vary between the UK/EU and US users. The findings are useful for guiding future strategic development of shared e-scooter services in different geographic contexts.

中文翻译：

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了解影响共享电动滑板车计划用户留存的因素：英国/欧盟和美国的比较研究

共享电动滑板车越来越被认为是应对城市交通挑战（包括拥堵和空气污染）的可持续出行方式。它们在美国城市的早期采用，随后在欧洲的扩张和在英国的持续试验，凸显了了解该计划成功和发展的关键因素的必要性，特别是关于现有用户的接受和保留。本研究采用一种可解释的机器学习方法来预测和了解当前用户持续使用共享电动滑板车的可能性。它提供了对影响用户留存率的重要因素的见解，同时还比较了英国/欧盟和美国的情况。开发的模型表现出很高的预测准确性，并强调了电动滑板车的感知实用性和用户友好性是影响用户未来使用意愿的关键决定因素。该研究阐明了心理特征，强调了绿色意识和个人价值观对用户留存率的影响，以及它们的重要性在英国/欧盟和美国用户之间如何有所不同。研究结果有助于指导共享电动滑板车服务在不同地理背景下的未来战略发展。