Discovering Mobility Patterns on Bicycle-Based Public Transportation System by Using Probabilistic Topic Models

  • Raul MontoliuEmail author
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 153)


In this work, we present a new framework to discover the daily mobility routines which are contained in a real-life dataset collected from a bike-sharing system. Our goal is the discovery and analysis of mobility patterns which characterize the behavior of the stations of a bike-sharing system based on the number of available bikes along a day. An unsupervised methodology based on probabilistic topic models has been used to achieve these goals. Topic models are probabilistic generative models for documents that identify the latent structure that underlies a set of words. In particular, Latent Dirichlet allocation (LDA) has been used to discover mobility patterns. Our database has been collected for almost half a year from the Bicicas bike sharing system in Castellón (Spain). A set of experiments have been conducted to demonstrate the type of patterns that can be effectively discovered by using the proposed framework.


Topic Model Latent Dirichlet Allocation Mobility Pattern Mobility Data Latent Topic 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of Machine Learning Research 3, 993–1022 (2003)zbMATHGoogle Scholar
  2. 2.
    Dearnaley, M.: Agency seeks substitute for nextbike’s shelved hire fleet (2011), (accessed January 20, 2011)
  3. 3.
    Farrahi, K., Gatica-Perez, D.: Discovering routines from large-scale human locations using probabilistic topic models. ACM Transactions on Intelligent Systems and Technology 2, 3:1–3:27 (2011)Google Scholar
  4. 4.
    Froehlich, J., Neumann, J., Oliver, N.: Measuring the pulse of the city through shared bicycle programs. In: Proceedings of International Workshop on Urban, Community, and Social Applications of Networked Sensing Systems (UrbanSense 2008) (2008)Google Scholar
  5. 5.
    Froehlich, J., Neumann, J., Oliver, N.: Sensing and predicting the pulse of the city through shared bicycling. In: Proceeding of the Twenty-First International Joint Conference on Artificial Intelligence (IJCAI 2009) (2009)Google Scholar
  6. 6.
    Griffiths, T.L., Steyvers, M.: Finding scientific topics. Proceedings of the National Academy of Sciences of the United States of America 101(1), 5228–5235 (2004)CrossRefGoogle Scholar
  7. 7.
    Kaltenbrunner, A., Meza, R., Grivolla, J., Codina, J., Banchs, R.: Urban cycles and mobility patterns: Exploring and predicting trends in a bicycle-based public transport system. Pervasive and Mobile Computing 6(4), 455–466 (2010)CrossRefGoogle Scholar
  8. 8.
    Niebles, J., Wang, H., Fei-Fei, L.: Unsupervised learning of human action categories using spatial-temporal words. International Journal of Computer Vision 79, 299–318 (2008)CrossRefGoogle Scholar
  9. 9.
    Sudderth, E.B., Torralba, A., Freeman, W.T., Willsky, A.S.: Describing visual scenes using transformed objects and parts. International Journal on Computer Vision 77, 291–330 (2008)CrossRefGoogle Scholar
  10. 10.
    Wang, X., Ma, X., Grimson, W.: Unsupervised activity perception in crowded and complicated scenes using hierarchical bayesian models. IEEE Transactions on Pattern Analysis and Machine Intelligence 31(3), 539–555 (2009)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.Institute of New Imaging Technologies (INIT)Jaume I UniversityCastellónSpain

Personalised recommendations