Latest revision as of 02:12, 2 February 2021

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

• https://hal.archives-ouvertes.fr/hal-01259562

• 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://dblp.uni-trier.de/db/conf/huc/ubicomp2015.html#ChenZPMYKZL15,

https://dl.acm.org/citation.cfm?doid=2750858.2804291,

https://doi.acm.org/10.1145/2750858.2804291,

http://longbiaochen.com/files/ubicomp-2015.pdf,

http://repository.ust.hk/ir/Record/1783.1-77402,

https://core.ac.uk/display/155710454,

https://academic.microsoft.com/#/detail/2004683304

• http://dl.acm.org/ft_gateway.cfm?id=2804291&ftid=1619176&dwn=1,

http://dx.doi.org/10.1145/2750858.2804291 under the license http://www.acm.org/publications/policies/copyright_policy#Background