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dc.contributor.author
Guidon, Sergio
dc.contributor.author
Reck, Daniel Jan
dc.contributor.author
Axhausen, Kay W.
dc.date.accessioned
2021-03-31T07:22:55Z
dc.date.available
2019-10-17T09:11:53Z
dc.date.available
2019-10-24T08:58:58Z
dc.date.available
2021-03-31T07:22:55Z
dc.date.issued
2020-01
dc.identifier.uri
http://hdl.handle.net/20.500.11850/371146
dc.description.abstract
The number of bicycle-sharing systems has undergone strong growth in the last two decades. This growth is part of a worldwide trend that has began in the 1990s and has strongly accelerated after 2005. Early bicycle-sharing systems have mainly been provided as a public service by cities, but meanwhile major international bicycle-sharing companies have emerged that seek to expand their operations to new cities. Two major strategic questions that arise are which cities should be considered for an expansion and the geographical extent of the service area. An important factor to support these decisions is expected demand for bicycle-sharing, as it is directly related to potential revenue. In this paper, booking data from an electric bicycle-sharing system was used to estimate and assess models for bicycle-sharing demand and to make predictions for an expansion to a new city. Employment, population, bars and restaurants and distance to a central location were among the most important predictors in terms of variance explained in the same city. However, omitting centrality measures improved predictions for the new city.
en_US
dc.language.iso
en
en_US
dc.publisher
Transportation Research Board
en_US
dc.subject
E-bike sharing
en_US
dc.subject
Bicycle-sharing
en_US
dc.subject
Demand prediction
en_US
dc.subject
Sharing economy
en_US
dc.subject
Spatial regression
en_US
dc.subject
Machine learning
en_US
dc.subject
Random forests
en_US
dc.title
Expanding a (electric) bicycle-sharing system to a new city
en_US
dc.type
Other Conference Item
ethz.title.subtitle
Prediction of demand with spatial regression and random forests
en_US
ethz.book.title
2020 TRB Annual Meeting Online
en_US
ethz.pages.start
20-01336
en_US
ethz.event
99th Annual Meeting of the Transportation Research Board (TRB 2020)
en_US
ethz.event.location
Washington, DC
en_US
ethz.event.date
January 12-16, 2020
en_US
ethz.notes
Poster presentation on January 15, 2020
en_US
ethz.publication.place
Washington, DC
en_US
ethz.publication.status
published
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00003 - Schulleitung und Dienste::00030 - Bereich Rektorat / Domain Rectorate::02890 - Inst. of Science, Technology, and Policy / Inst. of Science, Technology, and Policy
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02115 - Dep. Bau, Umwelt und Geomatik / Dep. of Civil, Env. and Geomatic Eng.::02610 - Inst. f. Verkehrspl. u. Transportsyst. / Inst. Transport Planning and Systems::03521 - Axhausen, Kay W. / Axhausen, Kay W.
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02100 - Dep. Architektur / Dep. of Architecture::02655 - Netzwerk Stadt und Landschaft D-ARCH
en_US
ethz.leitzahl.certified
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02115 - Dep. Bau, Umwelt und Geomatik / Dep. of Civil, Env. and Geomatic Eng.::02610 - Inst. f. Verkehrspl. u. Transportsyst. / Inst. Transport Planning and Systems::03521 - Axhausen, Kay W. / Axhausen, Kay W.
en_US
ethz.relation.isNewVersionOf
10.3929/ethz-b-000356142
ethz.date.deposited
2019-10-17T09:12:01Z
ethz.source
FORM
ethz.eth
yes
en_US
ethz.identifier.internal
1446
en_US
ethz.availability
Metadata only
en_US
ethz.rosetta.installDate
2021-03-31T07:23:45Z
ethz.rosetta.lastUpdated
2022-03-29T06:07:29Z
ethz.rosetta.versionExported
true
ethz.COinS
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