ABSTRACT

Dockless e-scooter sharing, as a new shared micromobility service, has quickly gained popularity in recent years. In this paper, we present a practical approach to estimating e-scooter flow patterns without knowing the actual routes taken by the e-scooter riders. Our method takes advantage of a huge open dataset that contains the origins and destinations of millions of trips. We show that our models can help cities better support the emerging shared micromobility service. The additional information generated in the modeling process can also be useful for a more refined analysis of e-scooter trips.

中文翻译：

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使用大数据估计电动滑板车交通流量以支持微交通规划

摘要

无桩共享电动滑板车作为一种新型的共享微出行服务，近年来迅速普及。在本文中，我们提出了一种在不知道电动滑板车骑手实际路线的情况下估计电动滑板车流动模式的实用方法。我们的方法利用了一个巨大的开放数据集，其中包含数百万次旅行的起点和终点。我们展示了我们的模型可以帮助城市更好地支持新兴的共享微型交通服务。建模过程中生成的附加信息也可用于对电动滑板车行程进行更精细的分析。