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Variability in daily activity-travel patterns: the case of a one-week travel diary

Author

Listed:
  • Charles Raux

    (LAET - Laboratoire Aménagement Économie Transports - UL2 - Université Lumière - Lyon 2 - ENTPE - École Nationale des Travaux Publics de l'État - CNRS - Centre National de la Recherche Scientifique)

  • Tai-Yu Ma

    (LISER - Luxembourg Institute of Socio-Economic Research)

  • Eric Cornelis

    (Groupe de recherche sur les transports - FUNDP - Facultés Universitaires Notre Dame de la Paix)

Abstract

Introduction: Understanding temporal rhythms in travel and activity patterns has been recognized as an important issue for the effective management of urban congestion. Research issues related to this topic concern the degree to which travel behaviour varies from one day to another, the differences between weekday and weekend travel, and the determinants of variability. Thanks to a seven-day travel diary collected for 707 individuals in the city of Ghent (Belgium) in 2008, this study goes further by studying this variability according to various time periods within the week and by analysing interpersonal and intrapersonal variations according to the varying attributes of activity-travel patterns. Methods: Different variance indicators and the sequential alignment method are applied for the measurement of variability of travel-activity behaviour. Moreover, the influence of individual characteristics on these variations is examined. ResultsThe overall picture of a large intrinsic variability in travel behaviour (i.e. trip or home-based tour generation) is confirmed. There is more difference in the number of trips per day for a given individual depending on the various days of week than there is between individuals per se, not including the weekend period, and this aspect is reinforced when considering home-based tours. Unlike the case of trip generation, there is greater difference between persons in their daily time allocation to various activities than between days for a given person in general, either during working days or during the weekend. This is also the case for daily activity sequence. Finally, the influence of socio-demographic characteristics on intrapersonal variability is weak, whether for daily trips, tours, time use or activity sequence. ConclusionsThe large level of intrapersonal variability in daily trip numbers already demonstrated in the literature is confirmed. Systematic day-to-day variability is shown to have an extremely low share in intrapersonal variability. The global picture is that intrapersonal variability is large while systematic day-to-day variability is marginal. Moreover, a striking result is that socio-demographic characteristics are mostly unable to explain the level of intrapersonal variability. The results reveal that individual behaviour is neither completely habitual nor completely random. On the one hand, intrapersonal variability is more important than the interpersonal one as regards daily trip numbers for the realization of mobility needs. On the other hand, activity time allocation and sequencing show an inverse trend, which can be linked with the habitual part of behaviour and the social role of the individual (through e.g. work, childcare and other activities).

Suggested Citation

  • Charles Raux & Tai-Yu Ma & Eric Cornelis, 2016. "Variability in daily activity-travel patterns: the case of a one-week travel diary," Post-Print halshs-01389479, HAL.
    • Handle: RePEc:hal:journl:halshs-01389479
    DOI: 10.1007/s12544-016-0213-9
    Note: View the original document on HAL open archive server: https://shs.hal.science/halshs-01389479
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    References listed on IDEAS

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    1. 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.
    2. W C Wilson, 1998. "Activity Pattern Analysis by Means of Sequence-Alignment Methods," Environment and Planning A, , vol. 30(6), pages 1017-1038, June.
    3. Raux, Charles & Ma, Tai-Yu & Joly, Iragaël & Kaufmann, Vincent & Cornelis, Eric & Ovtracht, Nicolas, 2011. "Travel and activity time allocation: An empirical comparison between eight cities in Europe," Transport Policy, Elsevier, vol. 18(2), pages 401-412, March.
    4. Robert Schlich & Kay Axhausen, 2003. "Habitual travel behaviour: Evidence from a six-week travel diary," Transportation, Springer, vol. 30(1), pages 13-36, February.
    5. Ron Buliung & Matthew Roorda & Tarmo Remmel, 2008. "Exploring spatial variety in patterns of activity-travel behaviour: initial results from the Toronto Travel-Activity Panel Survey (TTAPS)," Transportation, Springer, vol. 35(6), pages 697-722, November.
    6. Bhat, Chandra R. & Srinivasan, Sivaramakrishnan, 2005. "A multidimensional mixed ordered-response model for analyzing weekend activity participation," Transportation Research Part B: Methodological, Elsevier, vol. 39(3), pages 255-278, March.
    7. Clarke Wilson, 2008. "Activity patterns in space and time: calculating representative Hagerstrand trajectories," Transportation, Springer, vol. 35(4), pages 485-499, July.
    8. Joh, Chang-Hyeon & Arentze, Theo & Hofman, Frank & Timmermans, Harry, 2002. "Activity pattern similarity: a multidimensional sequence alignment method," Transportation Research Part B: Methodological, Elsevier, vol. 36(5), pages 385-403, June.
    9. Kang, Hejun & Scott, Darren M., 2010. "Exploring day-to-day variability in time use for household members," Transportation Research Part A: Policy and Practice, Elsevier, vol. 44(8), pages 609-619, October.
    10. Dick Ettema & Tanja Lippe, 2009. "Weekly rhythms in task and time allocation of households," Transportation, Springer, vol. 36(2), pages 113-129, March.
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    Cited by:

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    5. Yang Yang & Samitha Samaranayake & Timur Dogan, 2023. "A clustering-based approach to quantifying socio-demographic impacts on urban mobility patterns," Environment and Planning B, , vol. 50(9), pages 2452-2469, November.
    6. Oscar Egu & Patrick Bonnel, 2020. "Investigating day-to-day variability of transit usage on a multimonth scale with smart card data. A case study in Lyon," Post-Print halshs-03148937, HAL.
    7. Guoqiang Wu & Jinhyun Hong & Piyushimita Thakuriah, 2022. "Investigating the temporal changes in the relationships between time spent on the internet and non-mandatory activity-travel time use," Transportation, Springer, vol. 49(1), pages 213-235, February.
    8. Deschaintres, Elodie & Morency, Catherine & Trépanier, Martin, 2022. "Cross-analysis of the variability of travel behaviors using one-day trip diaries and longitudinal data," Transportation Research Part A: Policy and Practice, Elsevier, vol. 163(C), pages 228-246.
    9. Ballis, Haris & Dimitriou, Loukas, 2020. "Revealing personal activities schedules from synthesizing multi-period origin-destination matrices," Transportation Research Part B: Methodological, Elsevier, vol. 139(C), pages 224-258.

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