论文标题
通过离群值分析和可视化自行车共享需求
Analysing and visualising bike-sharing demand with outliers
论文作者
论文摘要
共享自行车是可持续城市流动性的流行组成部分。它需要预期的计划，例如电台位置和库存，以平衡预期的需求和能力。但是，外部因素（例如极端天气或公共交通中的故障）可能会导致需求偏离基线水平。确定此类离群值可以使历史数据可靠并改善预测。在本文中，我们展示了如何通过聚类站来识别离群值并应用功能深度分析。我们将分析技术应用于华盛顿特区的资本自行车数据集，作为整篇文章的运行示例，但我们的方法论是一般的设计。此外，我们提供了一系列有意义的可视化，以传达发现并突出需求中的模式。最后但并非最不重要的一点是，我们就如何使用需求预测和自行车共享计划过程中确定的异常值制定了管理建议。
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.
