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Analysing and visualising bike-sharing demand with outliers

  • 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.

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Metadaten
Author: Nicola Rennie, Catherine Cleophas, Adam Sykulski, Florian DostORCiD
URL:https://link.springer.com/article/10.1007/s44248-023-00001-z
DOI:https://doi.org/10.1007/s44248-023-00001-z
ISSN:2731-6955
Title of the source (English):Discover Data
Document Type:Scientific journal article peer-reviewed
Language:English
Year of publication:2023
Tag:Analytics; Data Visualization; Outlier Detection
Issue number:1
Article number:1
Way of publication:Open Access
Faculty/Chair:Fakultät 5 Wirtschaft, Recht und Gesellschaft / FG ABWL, insbesondere Marketing
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