Author:
Rennie Nicola,Cleophas Catherine,Sykulski Adam M.,Dost Florian
Abstract
AbstractBike-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.
Funder
EPSRC Center for Doctoral Training STOR-i Lancaster
Christian-Albrechts-Universität zu Kiel
Publisher
Springer Science and Business Media LLC
Cited by
3 articles.
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