skip to main content
10.1145/2818517.2818540acmotherconferencesArticle/Chapter ViewAbstractPublication Pagessiggraph-asiaConference Proceedingsconference-collections
other

Visualizing the time-varying crowd mobility

Published:02 November 2015Publication History

ABSTRACT

Modeling human mobility is a critical task in fields such as urban planning, ecology, and epidemiology. Given the current use of mobile phones, there is an abundance of data that can be used to create models of high reliability. Existing techniques can reveal the macro-patterns of crowd movement, or analyze the trajectory of an individual object; however, they focus on geographical characteristics. In this paper, we employ a novel data representation, the mobility transition graph, which is generated from a citywide human mobility dataset by defining the temporal trends of crowd mobility and the interleaved transitions between different mobility patterns. We describe the design, creation and manipulation of the mobility transition graph and demonstrate the efficiency of our approach by case study.

References

  1. Barabasi, A.-L. 2005. The origin of bursts and heavy tails in human dynamics. Nature 435, 7039, 207--211.Google ScholarGoogle Scholar
  2. de Montjoye, Y.-A., Hidalgo, C. A., Verleysen, M., and Blondel, V. D. 2013. Unique in the crowd: The privacy bounds of human mobility. Scientific reports 3.Google ScholarGoogle Scholar
  3. Gao, J., Goodman, J., Cao, G., and Li, H. 2002. Exploring asymmetric clustering for statistical language modeling. In Proceedings of the Association for Computational Linguistics, 183--190. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. Gonzalez, M. C., Hidalgo, C. A., and Barabasi, A.-L. 2008. Understanding individual human mobility patterns. Nature 453, 7196, 779--782.Google ScholarGoogle Scholar
  5. Schneider, C. M., Belik, V., Couronné, T., Smoreda, Z., and González, M. C. 2013. Unravelling daily human mobility motifs. Journal of The Royal Society Interface 10, 84, 2013--2046.Google ScholarGoogle ScholarCross RefCross Ref
  6. Song, L., Kolar, M., and Xing, E. P. 2009. Time-varying dynamic bayesian networks. In Advances in Neural Information Processing Systems, 1732--1740.Google ScholarGoogle Scholar
  7. Song, C., Qu, Z., Blumm, N., and Barabási, A.-L. 2010. Limits of predictability in human mobility. Science 327, 5968, 1018--1021.Google ScholarGoogle Scholar
  8. Wang, Z., Lu, M., Yuan, X., Zhang, J., and V. D. Wetering, H. 2013. Visual traffic jam analysis based on trajectory data. IEEE Transactions on Visualization and Computer Graphics 19, 12, 2159--2168. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. Xiong, H., Zhang, D., and Gauthier, V. 2012. Predicting mobile phone user locations by exploiting collective behavioral patterns. In IEEE Conference on Ubiquitous Intelligence and Computing, 164--171. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. Zeng, W., Fu, C.-W., Arisona, S. M., Erath, A., and Qu, H. 2014. Visualizing mobility of public transportation system. IEEE Transactions on Visualization and Computer Graphics 20, 12.Google ScholarGoogle ScholarCross RefCross Ref

Index Terms

  1. Visualizing the time-varying crowd mobility

      Recommendations

      Comments

      Login options

      Check if you have access through your login credentials or your institution to get full access on this article.

      Sign in
      • Published in

        cover image ACM Other conferences
        SA '15: SIGGRAPH Asia 2015 Visualization in High Performance Computing
        November 2015
        80 pages
        ISBN:9781450339292
        DOI:10.1145/2818517

        Copyright © 2015 Owner/Author

        Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

        Publisher

        Association for Computing Machinery

        New York, NY, United States

        Publication History

        • Published: 2 November 2015

        Check for updates

        Qualifiers

        • other

        Acceptance Rates

        Overall Acceptance Rate178of869submissions,20%

      PDF Format

      View or Download as a PDF file.

      PDF

      eReader

      View online with eReader.

      eReader