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Transportation

, Volume 35, Issue 4, pp 485–499 | Cite as

Activity patterns in space and time: calculating representative Hagerstrand trajectories

  • Clarke WilsonEmail author
Article

Abstract

Daily activity diaries can be recorded as sequences of characters representing events and their contexts as they unfold during the day. Dynamic programming algorithms as used in bioinformatics have been used by a number of researchers to measure the similarities and differences between travel patterns on the basis of temporal sequencing of events, activity transition, and total activity time. The resultant similarity matrices have been shown to be more effective in classifying sequential patterns than classifications based on alternative similarity indices. The basic algorithms can be amended to include the geographic coordinates of events by a suitable amendment to the definition of distance. This permits quantitative classification of Hagerstrand-type activity trajectories on the basis of both activity and spatial similarity. Such a classification can be used to group similar trajectories and to identify representative trajectories that are analogous to measures of central tendency in univariate statistics, giving more concrete meaning to the concept of the activity pattern than any other method now available. The paper illustrates the effect of considering both events and locations in the classification of daily activity patterns using activity diary data gathered in the town of Reading. The algorithm has been implemented in the Clustal_TXY alignment software package.

Keywords

Activity trajectories Alignment analysis Optimal matching Travel patterns 

Notes

Acknowledgements

This work was undertaken with the support of Research Development Initiatives grant #820-2003-2059 from the Social Science and Humanities Research Council of Canada. I am greatly indebted to Dr. Julie Thompson of the Institut de génétique et de biologie moléculaire et cellulaire, University of Strasbourg, who programmed the algorithm, for her continued interest in this research program. I also thank Dr. Andy Harvey of Saint Mary’s University, Halifax, who supported the original work on the ClustalG software. My wife’s cat, ET, also often helped.

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Copyright information

© Springer Science+Business Media, LLC. 2008

Authors and Affiliations

  1. 1.Canada Mortgage and Housing CorporationOttawaCanada

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