Title | Bike Sharing Station Placement Leveraging Heterogeneous Urban Open Data |
Authors | Chen, Longbiao Zhang, Daqing Pan, Gang Ma, Xiaojuan Yang, Dingqi Kushlev, Kostadin Zhang, Wangsheng Li, Shijian |
Affiliation | Zhejiang Univ, Hangzhou, Zhejiang, Peoples R China. Telecom SudParis, Inst Mines Telecom, CNRS, SAMOVAR, Evry, France. Hong Kong Univ Sci & Technol, Hong Kong, Hong Kong, Peoples R China. Peking Univ, Beijing, Peoples R China. Univ British Columbia, Vancouver, BC V5Z 1M9, Canada. Univ Fribourg, CH-1700 Fribourg, Switzerland. |
Keywords | Open data urban computing bike sharing system |
Issue Date | 2015 |
Publisher | ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) |
Citation | ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp).2015,571-575. |
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. |
URI | http://hdl.handle.net/20.500.11897/436678 |
DOI | 10.1145/2750858.2804291 |
Indexed | EI CPCI-S(ISTP) |
Appears in Collections: | 待认领 |