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Modeling activity scheduling time horizon: Duration of time between planning and execution of pre-planned activities

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  • Mohammadian, Abolfazl
  • Doherty, Sean T.

Abstract

Growing recognition that observed travel patterns are the result of an underlying activity scheduling process has resulted in a new stream of data collection and modeling efforts. Of particular focus is the planning or sequencing of activity scheduling decisions over time that precede actual execution of activities/trips. Understanding and potentially modeling these sequences offers particular promise, as strong interdependencies in activity/travel choices likely exist. In practice, however, a fixed order of sequencing by activity type is often assumed that overlooks the strong interdependencies in activity/travel choices and can be misleading. This study presents the process of developing parametric and non-parametric hazard models to predict the duration of time between planning and execution of pre-planned activities based on attributes of activity and characteristics of decision maker. Modeling results suggest that activity type alone may not suffice to fully explain how activities are planned. Rather, the nature of the activity and several overriding personal and situational factors play an important role. This will make the model more amenable to a variety of people and situations and will make it more sensitive to emerging policy action scenarios.

Suggested Citation

  • Mohammadian, Abolfazl & Doherty, Sean T., 2006. "Modeling activity scheduling time horizon: Duration of time between planning and execution of pre-planned activities," Transportation Research Part A: Policy and Practice, Elsevier, vol. 40(6), pages 475-490, July.
  • Handle: RePEc:eee:transa:v:40:y:2006:i:6:p:475-490
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    References listed on IDEAS

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    Cited by:

    1. Linda Nijland & Theo Arentze & Harry Timmermans, 2014. "Multi-day activity scheduling reactions to planned activities and future events in a dynamic model of activity-travel behavior," Journal of Geographical Systems, Springer, vol. 16(1), pages 71-87, January.
    2. 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.
    3. Erika Spissu & Abdul Pinjari & Chandra Bhat & Ram Pendyala & Kay Axhausen, 2009. "An analysis of weekly out-of-home discretionary activity participation and time-use behavior," Transportation, Springer, vol. 36(5), pages 483-510, September.
    4. Auld, Joshua & Mohammadian, Abolfazl (Kouros) & Doherty, Sean T., 2009. "Modeling activity conflict resolution strategies using scheduling process data," Transportation Research Part A: Policy and Practice, Elsevier, vol. 43(4), pages 386-400, May.
    5. Akar, Gulsah & Clifton, Kelly J. & Doherty, Sean T., 2012. "Redefining activity types: Who participates in which leisure activity?," Transportation Research Part A: Policy and Practice, Elsevier, vol. 46(8), pages 1194-1204.
    6. 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.
    7. Sean Doherty & Abolfazl Mohammadian, 2011. "The validity of using activity type to structure tour-based scheduling models," Transportation, Springer, vol. 38(1), pages 45-63, January.
    8. Abdul Rawoof Pinjari & Chandra R. Bhat, 2011. "Activity-based Travel Demand Analysis," Chapters,in: A Handbook of Transport Economics, chapter 10 Edward Elgar Publishing.

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