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:微型移动服务(例如电动自行车,电动滑板车)在城市社区中越来越受欢迎,这是一种灵活的交通选择,带来了机遇和挑战。作为一种不断增长的交通方式,从微型移动服务使用数据中获得的见解对政策制定和改善服务质量具有价值。现有的研究分析了不同地区使用分布的模式和特征,并集中于时间或空间方面。在本文中,我们采用一种同时分析电动滑板车出行的时空特征的方法,使我们能够在不同的时间框架和局部地理区域进行观察,这是以前的分析所不能做到的。从匿名、受限的共享电动滑板车骑行数据中获得的见解显示了所采用方法在受监管的、保护隐私的微移动出行数据上的适用性。我们的结果显示,人口密度是最重要的特征,并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出行呈负相关,这表明机动车辆所有者的电动滑板车使用量减少。此外,我们发现湿度的影响比降水量更重要,可预测每小时电动滑板车出行次数。缓冲区分析显示,近29%的行程停止了,27%的行程从人行道开始,揭示了墨尔本人们更多地利用人行道停放电动滑板车的情况。