Frontiers of Computer Science

, Volume 11, Issue 1, pp 38–48 | Cite as

Understanding bike trip patterns leveraging bike sharing system open data

  • Longbiao Chen
  • Xiaojuan Ma
  • Thi-Mai-Trang Nguyen
  • Gang Pan
  • Jérémie JakubowiczEmail author
Research Article


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.


bike sharing system open data ill-posed inverse problems urban computing 


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Copyright information

© Higher Education Press and Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  • Longbiao Chen
    • 1
    • 2
    • 3
  • Xiaojuan Ma
    • 4
  • Thi-Mai-Trang Nguyen
    • 2
  • Gang Pan
    • 3
  • Jérémie Jakubowicz
    • 1
    Email author
  1. 1.Institut Mines-Télécom, Télécom SudParisUMR CNRS SamovarEvryFrance
  2. 2.Laboratoire d’Informatique de Paris 6 (LIP6)University of Paris 6ParisFrance
  3. 3.College of Computer ScienceZhejiang UniversityHangzhouChina
  4. 4.Department of Computer Science and EngineeringHong Kong University of Science and TechnologyHong KongChina

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