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Frontiers of Computer Science

Front. Comput. Sci.    2017, Vol. 11 Issue (1) : 38-48     DOI: 10.1007/s11704-016-6006-4
Understanding bike trip patterns leveraging bike sharing system open data
Longbiao CHEN1,2,3,Xiaojuan MA4,Thi-Mai-Trang NGUYEN2,Gang PAN3,Jérémie JAKUBOWICZ1()
1. Institut Mines-Télécom, Télécom SudParis, UMR CNRS Samovar, Evry 91000, France
2. Laboratoire d’Informatique de Paris 6 (LIP6), University of Paris 6, Paris 75005, France
3. College of Computer Science, Zhejiang University, Hangzhou 310027, China
4. Department of Computer Science and Engineering, Hong Kong University of Science and Technology, Hong Kong, China
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Bike sharing systems are booming globally as a green and flexible transportationmode, but the flexibility also brings difficulties in keeping the bike stations balanced with enough bikes and docks. Understanding the spatio-temporal bike trip patterns in a bike sharing system, such as the popular trip origins and destinations during rush hours, is important for researchers to design models for bike scheduling and station management. However, due to privacy and operational concerns, bike trip data are usually not publicly available in many cities. Instead, the station feeds about real-time bike and dock number in stations are usually public, which we refer to as bike sharing system open data. In this paper, we propose an approach to infer the spatio-temporal bike trip patterns from the public station feeds. Since the number of possible trips (i.e., origin-destination station pairs) is much larger than the number of stations, we define the trip inference as an ill-posed inverse problem. To solve this problem, we identify the sparsity and locality properties of bike trip patterns, and propose a sparse and weighted regularization model to impose both properties in the solution. We evaluate our method using real-world data fromWashington, D.C. and New York City. Results show that our method can effectively infer the spatio-temporal bike trip patterns and outperform the baselines in both cities.

Keywords bike sharing system      open data      ill-posed inverse problems      urban computing     
Corresponding Authors: Jérémie JAKUBOWICZ   
Just Accepted Date: 23 August 2016   Online First Date: 05 December 2016    Issue Date: 11 January 2017
 Cite this article:   
Longbiao CHEN,Xiaojuan MA,Thi-Mai-Trang NGUYEN, et al. Understanding bike trip patterns leveraging bike sharing system open data[J]. Front. Comput. Sci., 2017, 11(1): 38-48.
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Longbiao CHEN
Xiaojuan MA
Thi-Mai-Trang NGUYEN
Gang PAN
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