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An empirical investigation on the dynamic processes of activity scheduling and trip chaining

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  • Ming Lee

  • Michael McNally

Abstract

Investigation of the dynamic processes of activity scheduling and trip chaining has been an interest of transportation researchers over the past decade because of its relevance to the effectiveness of congestion management and intelligent transportation systems. To empirically examine the processes, a computerized survey instrument is 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 the investigation on the dynamic processes of activity scheduling and trip chaining based on data collected from a pilot study of the instrument. With the data, ordered logit models are applied to identify factors that are pertinent to the scheduling horizon of activities. Results of the empirical analysis show that a daily schedule often starts with certain activities occupying a portion of the schedule and other activities are then arranged around these pre-occupants. Activities of shorter duration are more likely to be opportunistically inserted in a schedule already anchored by their longer duration counterparts. Persons with children often expect more constraining activities than those with no children. The analysis also shows that female respondents tend to be more structured in terms of how the week is planned. Additionally, analysis of travel patterns reveals that many trip-chains are formed opportunistically. Travel time required to reach an activity is positively related to the scheduling horizon for the activity, with more distant stops being planned earlier than closer locations. Copyright Springer Science+Business Media B.V. 2006

Suggested Citation

  • 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.
    • Handle: RePEc:kap:transp:v:33:y:2006:i:6:p:553-565
    DOI: 10.1007/s11116-006-7728-1
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    References listed on IDEAS

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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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    3. 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.
    4. Sean Doherty & Eric Miller, 2000. "A computerized household activity scheduling survey," Transportation, Springer, vol. 27(1), pages 75-97, February.
    5. Kitamura, Ryuichi, 1984. "Incorporating trip chaining into analysis of destination choice," Transportation Research Part B: Methodological, Elsevier, vol. 18(1), pages 67-81, February.
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    Cited by:

    1. Ruiz, Tomás & Habib, Khandker Nurul, 2016. "Scheduling decision styles on leisure and social activities," Transportation Research Part A: Policy and Practice, Elsevier, vol. 88(C), pages 304-317.
    2. Florian Schneider & Danique Ton & Lara-Britt Zomer & Winnie Daamen & Dorine Duives & Sascha Hoogendoorn-Lanser & Serge Hoogendoorn, 2021. "Trip chain complexity: a comparison among latent classes of daily mobility patterns," Transportation, Springer, vol. 48(2), pages 953-975, April.
    3. Khandker Habib, 2011. "A random utility maximization (RUM) based dynamic activity scheduling model: Application in weekend activity scheduling," Transportation, Springer, vol. 38(1), pages 123-151, January.
    4. 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.
    5. Auld, Joshua & Mohammadian, Abolfazl(Kouros), 2012. "Activity planning processes in the Agent-based Dynamic Activity Planning and Travel Scheduling (ADAPTS) model," Transportation Research Part A: Policy and Practice, Elsevier, vol. 46(8), pages 1386-1403.
    6. Joachim Scheiner & Christian Holz-Rau, 2017. "Women’s complex daily lives: a gendered look at trip chaining and activity pattern entropy in Germany," Transportation, Springer, vol. 44(1), pages 117-138, January.
    7. Harsh Shah & Andre L. Carrel & Huyen T. K. Le, 2024. "Impacts of teleworking and online shopping on travel: a tour-based analysis," Transportation, Springer, vol. 51(1), pages 99-127, February.
    8. Uğurel, Ekin & Huang, Shuai & Chen, Cynthia, 2024. "Learning to generate synthetic human mobility data: A physics-regularized Gaussian process approach based on multiple kernel learning," Transportation Research Part B: Methodological, Elsevier, vol. 189(C).
    9. 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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