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Transportation

, Volume 38, Issue 1, pp 65–79 | Cite as

Measuring dissimilarity of geographically dispersed space–time paths

  • Marlies Vanhulsel
  • Carolien Beckx
  • Davy Janssens
  • Koen Vanhoof
  • Geert Wets
Article

Abstract

In activity-travel analysis, sequences are analysed both in space and time. From this perspective, sequence alignment methods (SAM) are used to value the dissimilarity of sequences. However, only a limited number of research efforts account for spatial characteristics of activity-travel sequences. Additionally, the existing techniques considering spatial characteristics are mainly suited to compare sequences within a small study area. Therefore, the present research re-designs a multidimensional dissimilarity measure which enables identifying dissimilarities between sequences which are geographically dispersed. This technique includes transforming the geographical coordinates of activity locations to Angle/Arc Length (AAL)-trajectories to capture the relative geographical movements within each sequence. These AAL-trajectories form the basis of the subsequent multidimensional sequence alignment analysis aimed at estimating the dissimilarity between activity-travel sequences. This approach proves to compare activity-travel sequences based on the relative positions of the activity locations within sequences, rather than founded on the distances between the absolute geographical locations, as is the case in the traditional sequence alignment methods.

Keywords

Activity-travel sequences Sequence alignment method Spatio-temporal analysis Geographical movement 

Notes

Acknowledgments

The authors would like to thank the anonymous reviewers for their valuable comments, which allowed substantially improving the quality of this article.

References

  1. Algers, S., Eliasson, J., Mattsson, L.G.: Is it time to use activity-based urban transport models? A discussion of planning needs and modelling possibilities? Ann. Reg. Sci. 39(4), 767–789 (2005)CrossRefGoogle Scholar
  2. Bachi, R.: Standard distance measures and related methods for spatial analysis. Pap. Reg. Sci. Assoc. 10(1), 83–132 (1963)CrossRefGoogle Scholar
  3. Bellemans, T., Kochan, B., Janssens, D., Wets, G.: In the field evaluation of the impact of a gps-enabled personal digital assistant on activity-travel diary data quality. In: 87th Annual Meeting of the Transportation Research Board (TRB), Washington, DC, USA (2008)Google Scholar
  4. Buliung, R.N., Remmel, T.K.: Open source, spatial analysis, and activity-travel behaviour research: capabilities of the aspace package. J. Geogr. Syst. 10(2), 191–216 (2008)CrossRefGoogle Scholar
  5. Chapin, F.S.: Human Activity Patterns in the City, 1st edn. Wiley, New York, USA (1974)Google Scholar
  6. Fried, M.A., Havens, J., Thall, M.: Travel behavior—a synthesized theory. Tech. Rep., National Cooperative Highway Research Program—Transportation Research Board—National Research Council, Boston (1977)Google Scholar
  7. Gärling, T., Gillholm, R., Romanus, J., Selart, M.: Interdependent activity and travel choices: behavioural principles of integration of choice outcomes. In: Ettema, D., Timmermans, H.J. (eds.) Activity-Based Approaches to Travel-Analysis, 1st edn, pp. 135–150. Pergamon, Oxford, UK (1997)Google Scholar
  8. Hägerstrand, T.: What about people in regional science? Pap. Reg. Sci. Assoc. 24(1), 7–24 (1970)Google Scholar
  9. Janssens, D., Wets, G.: The presentation of an activity-based approach for surveying and modelling travel behaviour. In: 32nd Colloquium Vervoersplanologisch Speurwerk 2005: Duurzame mobiliteit: Hot or not?, vol. 32(2), pp. 1935–1945. Antwerp, Belgium (2005)Google Scholar
  10. Joh, C.H.: Measuring and predicting adaptation in multidimensional activity-travel patterns. Dissertation, Eindhoven University, The Netherlands (2004)Google Scholar
  11. Joh, C.H., Arentze, T.A., Timmermans, H.J.: Pattern recognition in complex activity-travel patterns: a comparison of euclidean distance, signal processing theoretical, and multidimensional sequence alignment methods. In: 80th Annual Meeting of the Transportation Research Board (TRB), Washington, DC, USA (2001)Google Scholar
  12. Joh, C.H., Arentze, T., Hofman, F., Timmermans, H.: Activity patterns similarity: a multidimensional sequence alignment method. Transp. Res. B Methodol. 36(2), 385–403 (2002)CrossRefGoogle Scholar
  13. Joh, C.H., Arentze, T., Timmermans, H.: Identifying skeletal information of activity patterns of a group. In: 86th Annual Meeting of the Transportation Research Board (TRB), Washington, DC, USA (2007)Google Scholar
  14. Jones, P.M.: New approaches to understanding travel behaviour: the human activity approach. In: Hensher, D., Stopher, P. (eds.) Behavioural Travel Modelling, 1st edn, pp. 55–80. Groom Helm Ltd, London, UK (1979)Google Scholar
  15. Kaufman, L., Rousseeuw, P.J.: Finding Groups in Data: An Introduction to Cluster Analysis. Wiley, Hoboken, USA (1990)Google Scholar
  16. Keogh, E.J., Pazzani, M.J.: Sixth ACM SIGKDD international conference on knowledge discovery and data mining, pp. 285–289. Boston, MA, USA (2000)Google Scholar
  17. Keogh, E., Chakrabarti, K., Pazzani, M., Mehrotra, S.: Dimensionality reduction for fast similarity search in large time series databases. J. Knowl. Inf. Syst. 3(3), 263–286 (2001)CrossRefGoogle Scholar
  18. Kitamura, R.: Applications of models of activity behavior for activity based demand forecasting. In: Activity-Based Travel Forecasting Conference: Summary, Recommendations and Compendium of Papers. Texas Transportation Institute, Arlington, TX, USA (1996)Google Scholar
  19. Kulkarni, A., McNally, M.G.: A microsimulation of daily activity patterns. In: 80th Annual Meeting of the Transportation Research Board (TRB), Washington, DC, USA (2001)Google Scholar
  20. Kwak, T.Y., Lee, Y.J.: A filtering method for searching similar multidimensional sequences under the time-warping distance. Inf. Syst. 28(7), 791–813 (2003)CrossRefGoogle Scholar
  21. Kwan, M.P.: Interactive geovisualization of activity-travel patterns using three-dimensional geographical information systems: a methodological exploration with a large data set. Transp. Res. C: Emerg. Technol. 8(1–6), 185–203 (2000)CrossRefGoogle Scholar
  22. Lefever, D.W.: Measuring geographic concentration by means of the standard deviational ellipse. Am. J. Sociol. 32(1), 88–94 (1926)CrossRefGoogle Scholar
  23. McIntosh, J., Yuan, M.: Assessing similarity of geographic processes and events. Trans. GIS. 9(2), 223–245 (2005)CrossRefGoogle Scholar
  24. McNally, M.G.: The activity-based approach. Tech. Rep. UCI-ITS-AS-WP-00-4, Center for Activity Systems Analysis, Irvine, California, USA. http://escholarship.org/uc/item/5sv5v9qt (2000).
  25. Pas, E.I.: A flexible and integrated methodology for analytical classification of daily activity-travel behavior. Transp. Sci. 17(4), 405–429 (1983)CrossRefGoogle Scholar
  26. Schlich, R.: Measurement issues in identifying variability in travel behaviour. In: 1st Swiss Transport Research Conference, Monte Verità, Ascona, Switzerland (2001)Google Scholar
  27. Schönfelder, S., Axhausen, K.W.: Activity spaces: measures of social exclusion? Transp. Policy 10(4), 273–286 (2003)CrossRefGoogle Scholar
  28. Shiftan, Y., Surhbier, J.: The analysis of travel and emission impacts of travel demand management strategies using activity-based models. Transportation 29(2), 145–168 (2002)CrossRefGoogle Scholar
  29. Shiftan, Y., Ben-Akiva, M.E., Proussaloglou, K., de Jong, G., Popuri, Y., Kasturirangan, K., Bekhor, S.: Activity-based modeling as a tool for better understanding travel behaviour. In: 10th International Conference on Travel Behaviour Research (IATBR), Lucerne, Swiss (2003)Google Scholar
  30. Stead, D., Banister, D.: Influencing mobility outside transport policy. Innov.: Eur. J. Soc. Sci. 14(4), 315–330 (2001)CrossRefGoogle Scholar
  31. Vlachos, M., Hadjieleftheriou, M., Gunopulos, D., Keogh, E.: Indexing multidimensional timeseries with support for multiple distance measures. In: International Conference on Knowledge Discovery and Data Mining, ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM, New York, USA. Washington, DC, USA (2003)Google Scholar
  32. Vlachos, M., Gunopulos, D., Das, G.: Rotation invariant distance measure for trajectories. In: 10th International Conference on Knowledge Discovery and Data Mining, Seattle, WA, USA (2004)Google Scholar
  33. Wilson, C.: Activity pattern analysis by means of sequence-alignment methods. Environ. Plan. A 30(6), 1017–1038 (1998a)CrossRefGoogle Scholar
  34. Wilson, C.: Analysis of travel behavior using sequence alignment methods. Transp. Res. Rec. 1645, 52–59 (1998b)CrossRefGoogle Scholar
  35. Wilson, C.: Activity patterns of Canadian women: application of clustalg sequence alignment software. Transp. Res. Rec. 1777, 55–67 (2001)CrossRefGoogle Scholar
  36. Wilson, C.: Activity patterns in space and time: calculating representative hagerstrand trajectories. Transportation 35(4), 485–499 (2008)CrossRefGoogle Scholar
  37. Yuill, R.S.: The standard deviation ellipse: an updated tool for spatial description. Geogr. Ann. B Hum. Geogr. 53(1), 28–39 (1971)CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC. 2010

Authors and Affiliations

  • Marlies Vanhulsel
    • 1
  • Carolien Beckx
    • 1
  • Davy Janssens
    • 2
  • Koen Vanhoof
    • 2
  • Geert Wets
    • 2
  1. 1.Flemish Institute for Technological Research (VITO)Antwerp, MolBelgium
  2. 2.Transportation Research Institute (IMOB), Hasselt UniversityHasseltBelgium

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