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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 prerequisite to an understanding changes in travel behavior. To examine this process, a web-based activity survey program, REACT!, was developed to collect household activity scheduling data. REACT! is unique in that it records the evolution of activity schedules from intentions to final outcomes for a multi-day period. This paper summarizes an investigation of the structure of activity/travel patterns based on a REACT! data set from a pilot study conducted in Irvine, California. The term structure refers to the outcome of a set of decisions facing individuals as they conduct their daily activities. At a minimum, structure can be interpreted as 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 longer duration counterparts. Additionally, analysis of tour structure reveals that many trip-chains were formed opportunistically. Travel time required to reach an activity was also 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," University of California Transportation Center, Working Papers qt15w464vp, University of California Transportation Center.
    • Handle: RePEc:cdl:uctcwp:qt15w464vp
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    1. Gärling, Tommy & Kwan, Mei-Po & Golledge, Reginald G., 1994. "Computational-process modelling of household activity scheduling," Transportation Research Part B: Methodological, Elsevier, vol. 28(5), pages 355-364, October.
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    Cited by:

    1. Ruiz, Tomás & Timmermans, Harry, 2008. "Changing the duration of activities in resolving scheduling conflicts," Transportation Research Part A: Policy and Practice, Elsevier, vol. 42(2), pages 347-359, February.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    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. 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.
    10. 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.
    11. 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.
    12. Daniel Shefer, 2014. "Sustainable Transportation and Urban Development," ERSA conference papers ersa14p306, European Regional Science Association.
    13. 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.
    14. Cho, WooKeol & Chung, Jin-Hyuk & Kim, Jinhee, 2023. "Need-based approach for modeling multiday activity participation patterns and identifying the impact of activity/travel conditions," Transportation Research Part A: Policy and Practice, Elsevier, vol. 172(C).
    15. 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.
    16. 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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