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Spatiotemporal variation in travel regularity through transit user profiling

Author

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  • Ed Manley

    (University College London)

  • Chen Zhong

    (University College London)

  • Michael Batty

    (University College London)

Abstract

New smart card datasets are providing new opportunities to explore travel behaviour in much greater depth than anything accomplished hitherto. Part of this quest involves measuring the great array of regular patterns within such data and explaining these relative to less regular patterns which have often been treated in the past as noise. Here we use a simple method called DBSCAN to identify clusters of travel events associated with particular individuals whose behaviour over space and time is captured by smart card data. Our dataset is a sequence of three months of data recording when and where individual travellers start and end rail and bus travel in Greater London. This dataset contains some 640 million transactions during the period of analysis we have chosen and it enables us to begin a search for regularities at the most basic level. We first define measures of regularity in terms of the proportions of events associated with temporal, modal (rail and bus), and service regularity clusters, revealing that the frequency distributions of these clusters follow skewed distributions with different means and variances. The analysis then continues to examine how regularity relative to irregular travel across space, demonstrating high regularities in the origins of trips in the suburbs contrasted with high regularities in the destinations in central London. This analysis sets the agenda for future research into how we capture and measure the differences between regular and irregular travel which we discuss by way of conclusion.

Suggested Citation

  • Ed Manley & Chen Zhong & Michael Batty, 2018. "Spatiotemporal variation in travel regularity through transit user profiling," Transportation, Springer, vol. 45(3), pages 703-732, May.
  • Handle: RePEc:kap:transp:v:45:y:2018:i:3:d:10.1007_s11116-016-9747-x
    DOI: 10.1007/s11116-016-9747-x
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    References listed on IDEAS

    as
    1. Marta C. González & César A. Hidalgo & Albert-László Barabási, 2009. "Understanding individual human mobility patterns," Nature, Nature, vol. 458(7235), pages 238-238, March.
    2. Kay Axhausen & Andrea Zimmermann & Stefan Schönfelder & Guido Rindsfüser & Thomas Haupt, 2002. "Observing the rhythms of daily life: A six-week travel diary," Transportation, Springer, vol. 29(2), pages 95-124, May.
    3. Minnen, Joeri & Glorieux, Ignace & van Tienoven, Theun Pieter, 2015. "Transportation habits: Evidence from time diary data," Transportation Research Part A: Policy and Practice, Elsevier, vol. 76(C), pages 25-37.
    4. Frank Primerano & Michael Taylor & Ladda Pitaksringkarn & Peter Tisato, 2008. "Defining and understanding trip chaining behaviour," Transportation, Springer, vol. 35(1), pages 55-72, January.
    5. Robert Schlich & Kay Axhausen, 2003. "Habitual travel behaviour: Evidence from a six-week travel diary," Transportation, Springer, vol. 30(1), pages 13-36, February.
    6. Manley, E.J. & Addison, J.D. & Cheng, T., 2015. "Shortest path or anchor-based route choice: a large-scale empirical analysis of minicab routing in London," Journal of Transport Geography, Elsevier, vol. 43(C), pages 123-139.
    7. Chen Zhong & Michael Batty & Ed Manley & Jiaqiu Wang & Zijia Wang & Feng Chen & Gerhard Schmitt, 2016. "Variability in Regularity: Mining Temporal Mobility Patterns in London, Singapore and Beijing Using Smart-Card Data," PLOS ONE, Public Library of Science, vol. 11(2), pages 1-17, February.
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    10. Zuoxian Gan & Min Yang & Tao Feng & Harry Timmermans, 2020. "Understanding urban mobility patterns from a spatiotemporal perspective: daily ridership profiles of metro stations," Transportation, Springer, vol. 47(1), pages 315-336, February.
    11. Gutiérrez, Aaron & Domènech, Antoni & Zaragozí, Benito & Miravet, Daniel, 2020. "Profiling tourists' use of public transport through smart travel card data," Journal of Transport Geography, Elsevier, vol. 88(C).
    12. Zhang, Yongping & Manley, Ed & Martens, Karel & Batty, Michael, 2024. "A metro smart card data-based analysis of group travel behaviour in Shanghai, China," Journal of Transport Geography, Elsevier, vol. 114(C).
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