Analysing and visualising bike-sharing demand with outliers

Rennie, Nicola and Cleophas, Catherine and Sykulski, Adam M. and Dost, Florian (2023) Analysing and visualising bike-sharing demand with outliers. Discover Data, 1 (1). ISSN 2731-6955

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Abstract

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.

Item Type:
Journal Article
Journal or Publication Title:
Discover Data
Subjects:
ID Code:
188317
Deposited By:
Deposited On:
07 Mar 2023 09:25
Refereed?:
Yes
Published?:
Published
Last Modified:
18 May 2023 14:10