Bike sharing station placement leveraging heterogeneous urban open data

Longbiao Chen; Daqing Zhang; Gang Pan; Xiaojuan Ma; Dingqi Yang; Kostadin Kushlev; Wangsheng Zhang; Shijian Li

doi:10.1145/2750858.2804291

Bike sharing station placement leveraging heterogeneous urban open data

Longbiao Chen
, Daqing Zhang
, Gang Pan
, Xiaojuan Ma
, Dingqi Yang
, Kostadin Kushlev
, Wangsheng Zhang
, Shijian Li

Zhejiang University
Institut Mines-Télécom
Tsinghua University
The Hong Kong University of Science and Technology
University of Fribourg
University of British Columbia

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

121 Citations (Scopus)

Abstract

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 stations.

Original language	English
Title of host publication	UbiComp 2015 - Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing
Publisher	Association for Computing Machinery, Inc
Pages	571-575
Number of pages	5
ISBN (Electronic)	9781450335744
DOIs	https://doi.org/10.1145/2750858.2804291
Publication status	Published - 7 Sept 2015
Externally published	Yes
Event	3rd ACM International Joint Conference on Pervasive and Ubiquitous Computing, UbiComp 2015 - Osaka, Japan Duration: 7 Sept 2015 → 11 Sept 2015

Publication series

Name	UbiComp 2015 - Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing

Conference

Conference	3rd ACM International Joint Conference on Pervasive and Ubiquitous Computing, UbiComp 2015
Country/Territory	Japan
City	Osaka
Period	7/09/15 → 11/09/15

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being
SDG 11 Sustainable Cities and Communities

Keywords

Bike sharing system
Open data
Urban computing

Access to Document

10.1145/2750858.2804291

Cite this

Chen, L., Zhang, D., Pan, G., Ma, X., Yang, D., Kushlev, K., Zhang, W., & Li, S. (2015). Bike sharing station placement leveraging heterogeneous urban open data. In UbiComp 2015 - Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing (pp. 571-575). (UbiComp 2015 - Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing). Association for Computing Machinery, Inc. https://doi.org/10.1145/2750858.2804291

Chen, Longbiao ; Zhang, Daqing ; Pan, Gang et al. / Bike sharing station placement leveraging heterogeneous urban open data. UbiComp 2015 - Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing. Association for Computing Machinery, Inc, 2015. pp. 571-575 (UbiComp 2015 - Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing).

@inproceedings{a5556f627db84014b8a8a7cf71d4cce3,

title = "Bike sharing station placement leveraging heterogeneous urban open data",

abstract = "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 stations.",

keywords = "Bike sharing system, Open data, Urban computing",

author = "Longbiao Chen and Daqing Zhang and Gang Pan and Xiaojuan Ma and Dingqi Yang and Kostadin Kushlev and Wangsheng Zhang and Shijian Li",

note = "Publisher Copyright: Copyright {\textcopyright} 2015 ACM.; 3rd ACM International Joint Conference on Pervasive and Ubiquitous Computing, UbiComp 2015 ; Conference date: 07-09-2015 Through 11-09-2015",

year = "2015",

month = sep,

day = "7",

doi = "10.1145/2750858.2804291",

language = "English",

series = "UbiComp 2015 - Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing",

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pages = "571--575",

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}

Chen, L, Zhang, D, Pan, G, Ma, X, Yang, D, Kushlev, K, Zhang, W & Li, S 2015, Bike sharing station placement leveraging heterogeneous urban open data. in UbiComp 2015 - Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing. UbiComp 2015 - Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing, Association for Computing Machinery, Inc, pp. 571-575, 3rd ACM International Joint Conference on Pervasive and Ubiquitous Computing, UbiComp 2015, Osaka, Japan, 7/09/15. https://doi.org/10.1145/2750858.2804291

Bike sharing station placement leveraging heterogeneous urban open data. / Chen, Longbiao; Zhang, Daqing; Pan, Gang et al.
UbiComp 2015 - Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing. Association for Computing Machinery, Inc, 2015. p. 571-575 (UbiComp 2015 - Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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T1 - Bike sharing station placement leveraging heterogeneous urban open data

AU - Chen, Longbiao

AU - Zhang, Daqing

AU - Pan, Gang

AU - Ma, Xiaojuan

AU - Yang, Dingqi

AU - Kushlev, Kostadin

AU - Zhang, Wangsheng

AU - Li, Shijian

PY - 2015/9/7

Y1 - 2015/9/7

N2 - 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 stations.

AB - 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 stations.

KW - Bike sharing system

KW - Open data

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M3 - Conference contribution

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ER -

Chen L, Zhang D, Pan G, Ma X, Yang D, Kushlev K et al. Bike sharing station placement leveraging heterogeneous urban open data. In UbiComp 2015 - Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing. Association for Computing Machinery, Inc. 2015. p. 571-575. (UbiComp 2015 - Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing). doi: 10.1145/2750858.2804291

Bike sharing station placement leveraging heterogeneous urban open data

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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Cite this