IDEAS home Printed from https://ideas.repec.org/p/hal/journl/halshs-01389479.html
   My bibliography  Save this paper

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
    as

    Download full text from publisher

    File URL: https://shs.hal.science/halshs-01389479/document
    Download Restriction: no

    File URL: https://libkey.io/10.1007/s12544-016-0213-9?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Xia Zhao & Mengying Cui & David Levinson, 2023. "Exploring temporal variability in travel patterns on public transit using big smart card data," Environment and Planning B, , vol. 50(1), pages 198-217, January.
    2. Wu, Guoqiang & Hong, Jinhyun, 2022. "An analysis of the role of residential location on the relationships between time spent online and non-mandatory activity-travel time use over time," Journal of Transport Geography, Elsevier, vol. 102(C).
    3. Thomas, Tom & La Paix Puello, Lissy & Geurs, Karst, 2019. "Intrapersonal mode choice variation: Evidence from a four-week smartphone-based travel survey in the Netherlands," Journal of Transport Geography, Elsevier, vol. 76(C), pages 287-300.
    4. 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.
    5. Benjamin Motte-Baumvol & Julie Fen-Chong & Olivier Bonin, 2023. "Immobility in a weekly mobility routine: studying the links between mobile and immobile days for employees and retirees," Transportation, Springer, vol. 50(5), pages 1723-1742, October.
    6. 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.
    7. 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.
    8. 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.
    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Charles Raux & Tai-Yu Ma & Eric Cornelis, 2011. "Variability versus stability in daily travel and activity behaviour. The case of a one week travel diary," Working Papers halshs-00612610, HAL.
    2. Dharmowijoyo, Dimas B.E. & Susilo, Yusak O. & Karlström, Anders, 2017. "Analysing the complexity of day-to-day individual activity-travel patterns using a multidimensional sequence alignment model: A case study in the Bandung Metropolitan Area, Indonesia," Journal of Transport Geography, Elsevier, vol. 64(C), pages 1-12.
    3. Yusak Susilo & Kay Axhausen, 2014. "Repetitions in individual daily activity–travel–location patterns: a study using the Herfindahl–Hirschman Index," Transportation, Springer, vol. 41(5), pages 995-1011, September.
    4. Jara-Díaz, Sergio & Rosales-Salas, Jorge, 2015. "Understanding time use: Daily or weekly data?," Transportation Research Part A: Policy and Practice, Elsevier, vol. 76(C), pages 38-57.
    5. 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.
    6. Dharmowijoyo, Dimas B.E. & Susilo, Yusak O. & Karlström, Anders & Adiredja, Lili Somantri, 2015. "Collecting a multi-dimensional three-weeks household time-use and activity diary in the Bandung Metropolitan Area, Indonesia," Transportation Research Part A: Policy and Practice, Elsevier, vol. 80(C), pages 231-246.
    7. Raux, Charles & Zoubir, Ayman & Geyik, Mirkan, 2017. "Who are bike sharing schemes members and do they travel differently? The case of Lyon’s “Velo’v” scheme," Transportation Research Part A: Policy and Practice, Elsevier, vol. 106(C), pages 350-363.
    8. Tana & Mei-Po Kwan & Yanwei Chai, 2016. "Urban form, car ownership and activity space in inner suburbs: A comparison between Beijing (China) and Chicago (United States)," Urban Studies, Urban Studies Journal Limited, vol. 53(9), pages 1784-1802, July.
    9. Heinen, Eva & Chatterjee, Kiron, 2015. "The same mode again? An exploration of mode choice variability in Great Britain using the National Travel Survey," Transportation Research Part A: Policy and Practice, Elsevier, vol. 78(C), pages 266-282.
    10. Dimas B. E. Dharmowijoyo & Yusak O. Susilo & Anders Karlström, 2016. "Day-to-day variability in travellers’ activity-travel patterns in the Jakarta metropolitan area," Transportation, Springer, vol. 43(4), pages 601-621, July.
    11. Arentze, Theo A. & Ettema, Dick & Timmermans, Harry J.P., 2011. "Estimating a model of dynamic activity generation based on one-day observations: Method and results," Transportation Research Part B: Methodological, Elsevier, vol. 45(2), pages 447-460, February.
    12. Huang, Yuqiao & Gao, Linjie & Ni, Anning & Liu, Xiaoning, 2021. "Analysis of travel mode choice and trip chain pattern relationships based on multi-day GPS data: A case study in Shanghai, China," Journal of Transport Geography, Elsevier, vol. 93(C).
    13. Crawford, F. & Watling, D.P. & Connors, R.D., 2018. "Identifying road user classes based on repeated trip behaviour using Bluetooth data," Transportation Research Part A: Policy and Practice, Elsevier, vol. 113(C), pages 55-74.
    14. Nursitihazlin Ahmad Termida & Yusak O. Susilo & Joel P. Franklin, 2016. "Examining the effects of out-of-home and in-home constraints on leisure activity participation in different seasons of the year," Transportation, Springer, vol. 43(6), pages 997-1021, November.
    15. Charles Raux & Ayman Zoubir, 2015. "Who are bike sharing schemes members and how they travel daily? The case of the Lyon’s “Velo’v” scheme," Working Papers halshs-01193169, HAL.
    16. Zhai, Wei & Bai, Xueyin & Peng, Zhong-ren & Gu, Chaolin, 2019. "From edit distance to augmented space-time-weighted edit distance: Detecting and clustering patterns of human activities in Puget Sound region," Journal of Transport Geography, Elsevier, vol. 78(C), pages 41-55.
    17. Perchoux, Camille & Kestens, Yan & Thomas, Frédérique & Hulst, Andraea Van & Thierry, Benoit & Chaix, Basile, 2014. "Assessing patterns of spatial behavior in health studies: Their socio-demographic determinants and associations with transportation modes (the RECORD Cohort Study)," Social Science & Medicine, Elsevier, vol. 119(C), pages 64-73.
    18. Milad Mehdizadeh & Alireza Ermagun, 2020. "“I’ll never stop driving my child to school”: on multimodal and monomodal car users," Transportation, Springer, vol. 47(3), pages 1071-1102, June.
    19. Abdul Rawoof Pinjari & Chandra R. Bhat, 2011. "Activity-based Travel Demand Analysis," Chapters, in: André de Palma & Robin Lindsey & Emile Quinet & Roger Vickerman (ed.), A Handbook of Transport Economics, chapter 10, Edward Elgar Publishing.
    20. Hao Wu & David Levinson & Andrew Owen, 2021. "Commute mode share and access to jobs across US metropolitan areas," Environment and Planning B, , vol. 48(4), pages 671-684, May.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:hal:journl:halshs-01389479. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: CCSD (email available below). General contact details of provider: https://hal.archives-ouvertes.fr/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.