Skip to main content

Interpreting Pedestrian Behaviour by Visualising and Clustering Movement Data

  • Conference paper
Web and Wireless Geographical Information Systems (W2GIS 2013)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 7820))

Abstract

Recent technological advances have increased the quantity of movement data being recorded. While valuable knowledge can be gained by analysing such data, its sheer volume creates challenges. Geovisual analytics, which helps the human cognition process by using tools to reason about data, offers powerful techniques to resolve these challenges. This paper introduces such a geovisual analytics environment for exploring movement trajectories, which provides visualisation interfaces, based on the classic space-time cube. Additionally, a new approach, using the mathematical description of motion within a space-time cube, is used to determine the similarity of trajectories and forms the basis for clustering them. These techniques were used to analyse pedestrian movement. The results reveal interesting and useful spatiotemporal patterns and clusters of pedestrians exhibiting similar behaviour.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 49.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Dodge, S., Weibel, R., Forootan, E.: Revealing the physics of movement: Comparing the similarity of movement characteristics of different types of moving objects. Computers, Environment and Urban Systems 33(6), 419–434 (2009)

    Article  Google Scholar 

  2. Orlando, S., Orsini, R., Raffaetà, A., Roncato, A., Silvestri, C.: Trajectory data warehouses: design and implementation issues. Journal of Computing Science and Engineering 1(2), 240–261 (2007)

    Article  Google Scholar 

  3. Millonig, A., Gartner, G.: Identifying motion and interest patterns of shoppers for developing personalised wayfinding tools. Journal of Location Based Services 5(1), 3–21 (2011)

    Article  Google Scholar 

  4. Zheng, X., Zhong, T., Liu, M.: Modeling crowd evacuation of a building based on seven methodological approaches. Building and Environment 44(3), 437–445 (2009)

    Article  Google Scholar 

  5. Hoogendoorn, S.P., Bovy, P.H.L.: Pedestrian travel behavior modeling. Networks and Spatial Economics 5(2), 193–216 (2005)

    Article  MATH  Google Scholar 

  6. Spek, S.: Tracking tourists in historic city centres. In: Information and Communication Technologies in Tourism 2010, pp. 185–196 (2010)

    Google Scholar 

  7. Van Schaick, J.: Timespace Matters-Exploring the Gap Between Knowing About Activity Patterns of People and Knowing How to Design and Plan Urban Areas and Regions (2011)

    Google Scholar 

  8. Laube, P., Imfeld, S., Weibel, R.: Discovering relative motion patterns in groups of moving point objects. International Journal of Geographical Information Science 19(6), 639–668 (2005)

    Article  Google Scholar 

  9. Ashbrook, D., Starner, T.: Using gps to learn significant locations and predict movement across multiple users. Personal and Ubiquitous Computing 7(5), 275–286 (2003)

    Article  Google Scholar 

  10. Wilson, C.: Activity patterns in space and time: calculating representative hagerstrand trajectories. Transportation 35(4), 485–499 (2008)

    Article  Google Scholar 

  11. Dodge, S., Weibel, R., Lautenschütz, A.K.: Towards a taxonomy of movement patterns. Information Visualization 7(3-4), 240–252 (2008)

    Article  Google Scholar 

  12. Andrienko, G., Andrienko, N.: Spatio-temporal aggregation for visual analysis of movements. In: IEEE Symposium on Visual Analytics Science and Technology, VAST 2008, pp. 51–58. IEEE (2008)

    Google Scholar 

  13. Hägerstrand, T.: What about people in regional science? Papers in Regional Science 24(1), 6–21 (1970)

    Article  Google Scholar 

  14. Kraak, M.J.: The space-time cube revisited from a geovisualization perspective. In: Proc. 21st International Cartographic Conference, pp. 1988–1996 (2003)

    Google Scholar 

  15. Kapler, T., Wright, W.: Geotime information visualization. Information Visualization 4(2), 136–146 (2005)

    Article  Google Scholar 

  16. Kraak, M.J.: Geovisualization and time–new opportunities for the space–time cube. In: Geographic Visualization: Concepts, Tools and Applications, pp. 293–306 (2008)

    Google Scholar 

  17. Andrienko, G., Andrienko, N., Wrobel, S.: Visual analytics tools for analysis of movement data. ACM SIGKDD Explorations Newsletter 9(2), 38–46 (2007)

    Article  Google Scholar 

  18. Demšar, U., Virrantaus, K.: Space–time density of trajectories: exploring spatio-temporal patterns in movement data. International Journal of Geographical Information Science 24(10), 1527–1542 (2010)

    Article  Google Scholar 

  19. Andrienko, G., Andrienko, N.: Poster: Dynamic time transformation for interpreting clusters of trajectories with space-time cube. In: Proceedings of the IEEE Symposium on Visual Analytics Science and Technology (VAST) 2010, pp. 213–214 (2010)

    Google Scholar 

  20. Horne, J.S., Garton, E.O., Krone, S.M., Lewis, J.S.: Analyzing animal movements using brownian bridges. Ecology 88(9), 2354–2363 (2007)

    Article  Google Scholar 

  21. Slingsby, A., Strachan, J., Vidale, P.L., Dykes, J., Wood, J.: Discovery exhibition: Making hurricane track data accessible. Discovery Exhibition Entry (2010)

    Google Scholar 

  22. Frentzos, E., Gratsias, K., Pelekis, N., Theodoridis, Y.: Algorithms for nearest neighbor search on moving object trajectories. Geoinformatica 11(2), 159–193 (2007)

    Article  Google Scholar 

  23. Zhang, Z., Huang, K., Tan, T.: Comparison of similarity measures for trajectory clustering in outdoor surveillance scenes. In: 18th International Conference on Pattern Recognition, ICPR 2006, vol. 3, pp. 1135–1138. IEEE (2006)

    Google Scholar 

  24. Buchin, K., Buchin, M., Wenk, C.: Computing the fréchet distance between simple polygons in polynomial time. In: Proceedings of the Twenty-Second Annual Symposium on Computational Geometry, pp. 80–87. ACM (2006)

    Google Scholar 

  25. Sakurai, Y., Yoshikawa, M., Faloutsos, C.: Ftw: fast similarity search under the time warping distance. In: Proceedings of the Twenty-Fourth ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems, pp. 326–337. ACM (2005)

    Google Scholar 

  26. Vlachos, M., Kollios, G., Gunopulos, D.: Discovering similar multidimensional trajectories. In: Proceedings of the 18th International Conference on Data Engineering, pp. 673–684. IEEE (2002)

    Google Scholar 

  27. Chen, L., Özsu, M.T., Oria, V.: Robust and fast similarity search for moving object trajectories. In: Proceedings of the 2005 ACM SIGMOD International Conference on Management of Data, pp. 491–502. ACM (2005)

    Google Scholar 

  28. Halliday, D., Resnick, R., Walker, J.: Fundamentals of Physics. Wiley (1997)

    Google Scholar 

  29. Altiparmak, F., Ferhatosmanoglu, H., Erdal, S., Trost, D.C.: Information mining over heterogeneous and high-dimensional time-series data in clinical trials databases. IEEE Transactions on Information Technology in Biomedicine 10(2), 254–263 (2006)

    Article  Google Scholar 

  30. Jain, A.K., Murty, M.N., Flynn, P.J.: Data clustering: a review. ACM computing surveys (CSUR) 31(3), 264–323 (1999)

    Article  Google Scholar 

  31. Song, Y., Chen, W.-Y., Bai, H., Lin, C.-J., Chang, E.Y.: Parallel Spectral Clustering. In: Daelemans, W., Goethals, B., Morik, K. (eds.) ECML PKDD 2008, Part II. LNCS (LNAI), vol. 5212, pp. 374–389. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  32. Chen, W.Y., Song, Y., Bai, H., Lin, C.J., Chang, E.Y.: Parallel spectral clustering in distributed systems. IEEE Transactions on Pattern Analysis and Machine Intelligence 33(3), 568–586 (2011)

    Article  Google Scholar 

  33. Atev, S., Miller, G., Papanikolopoulos, N.P.: Clustering of vehicle trajectories. IEEE Transactions on Intelligent Transportation Systems 11(3), 647–657 (2010)

    Article  Google Scholar 

  34. Morris, B., Trivedi, M.: Learning trajectory patterns by clustering: Experimental studies and comparative evaluation. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2009, pp. 312–319. IEEE (2009)

    Google Scholar 

  35. Palma, A.T., Bogorny, V., Kuijpers, B., Alvares, L.O.: A clustering-based approach for discovering interesting places in trajectories. In: Proceedings of the 2008 ACM Symposium on Applied Computing, pp. 863–868. ACM (2008)

    Google Scholar 

  36. Lewis, J.R.: Ibm computer usability satisfaction questionnaires: psychometric evaluation and instructions for use. International Journal of Human-Computer Interaction 7(1), 57–78 (1995)

    Article  Google Scholar 

  37. Lewis, J.R.: Psychometric evaluation of an after-scenario questionnaire for computer usability studies: the asq. ACM SIGCHI Bulletin 23(1), 78–81 (1991)

    Article  Google Scholar 

  38. Shneiderman, B.: The eyes have it: A task by data type taxonomy for information visualizations. In: Proceedings of the IEEE Symposium on Visual Languages, pp. 336–343. IEEE (1996)

    Google Scholar 

  39. Van Schaick, J., Van Der Spek, S.C.: Urbanism on Track: Application of Tracking Technologies in Urbanism, vol. 1. Ios Press Inc. (2008)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2013 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

McArdle, G., Demšar, U., van der Spek, S., McLoone, S. (2013). Interpreting Pedestrian Behaviour by Visualising and Clustering Movement Data. In: Liang, S.H.L., Wang, X., Claramunt, C. (eds) Web and Wireless Geographical Information Systems. W2GIS 2013. Lecture Notes in Computer Science, vol 7820. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37087-8_6

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-37087-8_6

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-37086-1

  • Online ISBN: 978-3-642-37087-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics