Micro-mobility services (e.g., e-bikes, e-scooters) are increasingly popular among urban communities, being a flexible transport option that brings both opportunities and challenges. As a growing mode of transportation, insights gained from micro-mobility usage data are valuable in policy formulation and improving the quality of services. Existing research analyses patterns and features associated with usage distributions in different localities, and focuses on either temporal or spatial aspects. In this paper, we employ a combination of methods that analyse both spatial and temporal characteristics related to e-scooter trips in a more granular level, enabling observations at different time frames and local geographical zones that prior analysis wasn't able to do. The insights obtained from anonymised, restricted data on shared e-scooter rides show the applicability of the employed method on regulated, privacy preserving micro-mobility trip data. Our results showed population density is the topmost important feature, and it associates with e-scooter usage positively. Population owning motor vehicles is negatively associated with shared e-scooter trips, suggesting a reduction in e-scooter usage among motor vehicle owners. Furthermore, we found that the effect of humidity is more important than precipitation in predicting hourly e-scooter trip count. Buffer analysis showed, nearly 29% trips were stopped, and 27% trips were started on the footpath, revealing higher utilisation of footpaths for parking e-scooters in Melbourne.

翻译：微出行服务（如电动自行车、电动滑板车）在城市社区中日益普及，作为一种灵活的交通方式，既带来了机遇也带来了挑战。作为一种新兴交通模式，从微出行使用数据中获得的见解对政策制定和服务质量提升具有重要价值。现有研究分析了不同地区使用分布的模式与特征，但往往侧重于时间或空间单一维度。本文采用组合方法，从更细粒度层面分析电动滑板车行程的时空特征，从而能够在不同时间框架和地方地理区域进行观察，这是以往分析无法实现的。通过对共享电动滑板车匿名化受限数据的分析，验证了该方法在受监管且保护隐私的微出行行程数据中的适用性。结果表明，人口密度是最重要的特征，且与电动滑板车使用呈正相关。拥有机动车辆的人口与共享电动滑板车行程呈负相关，表明机动车拥有者中电动滑板车使用减少。此外，我们发现湿度对预测每小时电动滑板车行程数量的影响比降水更为重要。缓冲区分析显示，近29%的行程在人行道上结束，27%的行程在人行道上开始，这表明墨尔本的人行道被大量用于停放电动滑板车。