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On the structure of weekly activity/travel patterns

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  • Lee, Ming S.
  • McNally, Michael G.

Abstract

Understanding the process of activity scheduling is a critical pre-requisite to an understanding of changes in travel behavior. To examine this process, a computerized survey instrument was developed to collect household activity scheduling data. The instrument is unique in that it records the evolution of activity schedules from intentions to final outcomes for a weekly period. This paper summarizes an investigation of the structure of activity/travel patterns based on data collected from a pilot study of the instrument. The term "structure" refers to the sequence by which various activities enter one's daily activity scheduling process. Results of the empirical analyses show that activities of shorter duration were more likely to be opportunistically inserted in a schedule already anchored by their longer duration counterparts. Additionally, analysis of travel patterns reveals that many trip-chains were formed opportunistically. Travel time required to reach an activity was positively related to the scheduling horizon for the activity, with more distant stops being planned earlier than closer locations.

Suggested Citation

  • Lee, Ming S. & McNally, Michael G., 2003. "On the structure of weekly activity/travel patterns," Transportation Research Part A: Policy and Practice, Elsevier, vol. 37(10), pages 823-839, December.
    • Handle: RePEc:eee:transa:v:37:y:2003:i:10:p:823-839
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    References listed on IDEAS

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    Cited by:

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    2. 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.
    3. Astroza, Sebastian & Bhat, Prerna C. & Bhat, Chandra R. & Pendyala, Ram M. & Garikapati, Venu M., 2018. "Understanding activity engagement across weekdays and weekend days: A multivariate multiple discrete-continuous modeling approach," Journal of choice modelling, Elsevier, vol. 28(C), pages 56-70.
    4. Krygsman, Stephan & Arentze, Theo & Timmermans, Harry, 2007. "Capturing tour mode and activity choice interdependencies: A co-evolutionary logit modelling approach," Transportation Research Part A: Policy and Practice, Elsevier, vol. 41(10), pages 913-933, December.
    5. Rafiq, Rezwana & McNally, Michael G., 2020. "An empirical analysis and policy implications of work tours utilizing public transit," Transportation Research Part A: Policy and Practice, Elsevier, vol. 142(C), pages 237-259.
    6. Shen, Yue & Kwan, Mei-Po & Chai, Yanwei, 2013. "Investigating commuting flexibility with GPS data and 3D geovisualization: a case study of Beijing, China," Journal of Transport Geography, Elsevier, vol. 32(C), pages 1-11.
    7. Fang, Zhixiang & Tu, Wei & Li, Qingquan & Li, Qiuping, 2011. "A multi-objective approach to scheduling joint participation with variable space and time preferences and opportunities," Journal of Transport Geography, Elsevier, vol. 19(4), pages 623-634.
    8. Ming Lee & Michael McNally, 2006. "An empirical investigation on the dynamic processes of activity scheduling and trip chaining," Transportation, Springer, vol. 33(6), pages 553-565, November.
    9. Ilan Salomon & Matan E. Singer, 2014. "'Informal Travel': A New Conceptualization of Travel Patterns?," Transport Reviews, Taylor & Francis Journals, vol. 34(5), pages 562-582, September.
    10. Allahviranloo, Mahdieh & Aissaoui, Leila, 2019. "A comparison of time-use behavior in metropolitan areas using pattern recognition techniques," Transportation Research Part A: Policy and Practice, Elsevier, vol. 129(C), pages 271-287.
    11. Brand, Christian & Preston, John M., 2010. "'60-20 emission'--The unequal distribution of greenhouse gas emissions from personal, non-business travel in the UK," Transport Policy, Elsevier, vol. 17(1), pages 9-19, January.
    12. Limanond, Thirayoot & Jomnonkwao, Sajjakaj & Watthanaklang, Duangdao & Ratanavaraha, Vatanavongs & Siridhara, Siradol, 2011. "How vehicle ownership affect time utilization on study, leisure, social activities, and academic performance of university students? A case study of engineering freshmen in a rural university in Thail," Transport Policy, Elsevier, vol. 18(5), pages 719-726, September.
    13. François Sprumont & Ariane Scheffer & Geoffrey Caruso & Eric Cornelis & Francesco Viti, 2022. "Quantifying the Relation between Activity Pattern Complexity and Car Use Using a Partial Least Square Structural Equation Model," Sustainability, MDPI, vol. 14(19), pages 1-16, September.
    14. 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.
    15. Daniel Shefer, 2014. "Sustainable Transportation and Urban Development," ERSA conference papers ersa14p306, European Regional Science Association.
    16. Chow, Joseph Y.J. & Recker, Will W., 2012. "Inverse optimization with endogenous arrival time constraints to calibrate the household activity pattern problem," Transportation Research Part B: Methodological, Elsevier, vol. 46(3), pages 463-479.
    17. 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.

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