Scipediacontent: Scipediacontent moved page Draft Content 761222503 to Yang et al 2015d

2021-02-02T01:12:08Z

Scipediacontent moved page Draft Content 761222503 to Yang et al 2015d

Scipediacontent: Created page with " == Abstract == International audience; Bike sharing systems have been deployed in many cities to promote green transportation and a healthy lifestyle. One of the key factors..."

2021-02-02T01:12:04Z

Created page with " == Abstract == International audience; Bike sharing systems have been deployed in many cities to promote green transportation and a healthy lifestyle. One of the key factors..."

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== Abstract ==

International audience; Bike sharing systems have been deployed in many cities to promote green transportation and a healthy lifestyle. One of the key factors for maximizing the utility of such systems is placing bike stations at locations that can best meet users' trip demand. Traditionally, urban planners rely on dedicated surveys to understand the local bike trip demand, which is costly in time and labor, especially when they need to compare many possible places. In this paper, we formulate the bike station placement issue as a bike trip demand prediction problem. We propose a semi-supervised feature selection method to extract customized features from the highly variant, heterogeneous urban open data to predict bike trip demand. Evaluation using real-world open data from Washington, D.C. and Hangzhou shows that our method can be applied to different cities to effectively recommend places with higher potential bike trip demand for placing future bike station

== Original document ==

The different versions of the original document can be found in:

* [http://dx.doi.org/10.1145/2750858.2804291 http://dx.doi.org/10.1145/2750858.2804291]

* [https://hal.archives-ouvertes.fr/hal-01259562 https://hal.archives-ouvertes.fr/hal-01259562]

* [https://dl.acm.org/citation.cfm?id=2804291 https://dl.acm.org/citation.cfm?id=2804291],
: [https://www.researchgate.net/profile/Kostadin_Kushlev/publication/281848772_Bike_Sharing_Station_Placement_Leveraging_Heterogeneous_Urban_Open_Data/links/55fb26df08aeafc8ac41b757.pdf https://www.researchgate.net/profile/Kostadin_Kushlev/publication/281848772_Bike_Sharing_Station_Placement_Leveraging_Heterogeneous_Urban_Open_Data/links/55fb26df08aeafc8ac41b757.pdf],
: [https://dblp.uni-trier.de/db/conf/huc/ubicomp2015.html#ChenZPMYKZL15 https://dblp.uni-trier.de/db/conf/huc/ubicomp2015.html#ChenZPMYKZL15],
: [https://dl.acm.org/citation.cfm?doid=2750858.2804291 https://dl.acm.org/citation.cfm?doid=2750858.2804291],
: [https://doi.acm.org/10.1145/2750858.2804291 https://doi.acm.org/10.1145/2750858.2804291],
: [http://longbiaochen.com/files/ubicomp-2015.pdf http://longbiaochen.com/files/ubicomp-2015.pdf],
: [http://repository.ust.hk/ir/Record/1783.1-77402 http://repository.ust.hk/ir/Record/1783.1-77402],
: [https://core.ac.uk/display/155710454 https://core.ac.uk/display/155710454],
: [https://academic.microsoft.com/#/detail/2004683304 https://academic.microsoft.com/#/detail/2004683304]

* [http://dl.acm.org/ft_gateway.cfm?id=2804291&ftid=1619176&dwn=1 http://dl.acm.org/ft_gateway.cfm?id=2804291&ftid=1619176&dwn=1],
: [http://dx.doi.org/10.1145/2750858.2804291 http://dx.doi.org/10.1145/2750858.2804291] under the license http://www.acm.org/publications/policies/copyright_policy#Background

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Yang et al 2015d - Revision history

Scipediacontent: Scipediacontent moved page Draft Content 761222503 to Yang et al 2015d

Scipediacontent: Created page with " == Abstract == International audience; Bike sharing systems have been deployed in many cities to promote green transportation and a healthy lifestyle. One of the key factors..."