Bike-sharing is a popular component of sustainable urban mobility. It requires anticipatory planning, e.g. of station locations and inventory, to balance expected demand and capacity. However, external factors such as extreme weather or glitches in public transport, can cause demand to deviate from baseline levels. Identifying such outliers keeps historic data reliable and improves forecasts. In this paper we show how outliers can be identified by clustering stations and applying a functional depth analysis. We apply our analysis techniques to the Washington D.C. Capital Bikeshare data set as the running example throughout the paper, but our methodology is general by design. Furthermore, we offer an array of meaningful visualisations to communicate findings and highlight patterns in demand. Last but not least, we formulate managerial recommendations on how to use both the demand forecast and the identified outliers in the bike-sharing planning process.

翻译：共享单车是可持续城市交通的重要组成部分。它需要前瞻性规划（例如站点选址与库存配置）以平衡预期需求与运力。然而，极端天气或公共交通故障等外部因素可能导致需求偏离基准水平。识别此类异常值既能保障历史数据的可靠性，又能提升预测精度。本文通过聚类站点并应用函数型深度分析，展示了异常值的识别方法。我们以华盛顿特区首都共享单车数据集作为贯穿全文的实例进行分析，但所提出的方法论在设计上具有通用性。此外，我们提供了一系列有效的可视化工具用于传达分析发现并突显需求模式。最后，我们从管理实践角度提出建议，指导如何在共享单车规划过程中结合需求预测与识别出的异常值。