Abstract
The paper presents a modeling framework for dynamic activity scheduling. The modeling framework considers random utility maximization (RUM) assumption for its components in order to capture the joint activity type, location and continuous time expenditure choice tradeoffs over the course of the day. The dynamics of activity scheduling process are modeled by considering the history of activity participation as well as changes in time budget availability over the day. For empirical application, the model is estimated for weekend activity scheduling using a dataset (CHASE) collected in Toronto in 2002–2003. The data set classifies activities into nine general categories. For the empirical model of a 24-h weekend activity scheduling, only activity type and time expenditure choices are considered. The estimated empirical model captures many behavioral details and gives a high degree of fit to the observed weekend scheduling patterns. Some examples of such behavioral details are the effects of time of the day on activity type choice for scheduling and on the corresponding time expenditure; the effects of travel time requirements on activity type choice for scheduling and on the corresponding time expenditure, etc. Among many other findings, the empirical model reveals that on the weekend the utility of scheduling Recreational activities for later in the day and over a longer duration of time is high. It also reveals that on the weekend, Social activity scheduling is not affected by travel time requirements, but longer travel time requirements typically lead to longer-duration social activities.
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Notes
A reviewer comments that “activity execution” would be preferable to “activity scheduling”, because the model developed here operates only on knowledge of activities that have already occurred; it does not take into account the ability of specific planned future activities to affect present decisions (although it does take into account the total amount of time remaining in the day, in the form of a “composite activity”). For consistency, I have retained the “activity scheduling” terminology as used by Cirillo and Axhausen (2010) and Habib and Miller (2008) to refer to a similar conceptualization of the process.
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Acknowledgements
This research was partially funded by an NSERC (Natural Science and Engineering Research Council of Canada) Discovery Grant. The author would like to acknowledge the assistance of Tazul Islam for helping in data preparation and model validation. Suggestions provided by Professor Mokhtarian and three anonymous reviewers to improve the quality of the paper are also acknowledged.
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Habib, K.M.N. A random utility maximization (RUM) based dynamic activity scheduling model: Application in weekend activity scheduling. Transportation 38, 123–151 (2011). https://doi.org/10.1007/s11116-010-9294-9
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DOI: https://doi.org/10.1007/s11116-010-9294-9