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Dynamic Model of Weekly Activity Pattern

Author

Listed:
  • Moshe Hirsh

    (Northwestern University, Evanston, Illinois)

  • Joseph N. Prashkea

    (University of California, Irvine, California, and Technion-Israel Institute of Technology, Haifa, Israel)

  • Moshe Ben-Akiva

    (Massachusetts Institute of Technology, Cambridge, Massachusetts)

Abstract

This paper presents a model of weekly activity pattern, based on a theory of individual behavior. The week is divided into time periods, and the following dynamic decision-making process is suggested. At the beginning of the first period, the individual selects his/her activity pattern for the entire week. At the beginning of the second period, the individual updates his/her plans for the remaining periods of the week on the basis of the actual behavior and the additional information that was acquired during the first time periods. In this way, the individual proceeds from period to period and the observed weekly activity pattern is the outcome of successive decisions. Based on utility maximizing principles, a parametric model of this dynamic decision-making process that can be estimated with revealed preferences data is formulated. A version of the model for weekly shopping activity behavior is estimated with survey data from Israel. The model is then applied to predict the effects of shortening the workweek. The empirical results support the dynamic behavior hypothesis and demonstrate the potential biases that may arise from the omission in a travel demand model of the interdependencies among the days of the week.

Suggested Citation

  • Moshe Hirsh & Joseph N. Prashkea & Moshe Ben-Akiva, 1986. "Dynamic Model of Weekly Activity Pattern," Transportation Science, INFORMS, vol. 20(1), pages 24-36, February.
    • Handle: RePEc:inm:ortrsc:v:20:y:1986:i:1:p:24-36
    DOI: 10.1287/trsc.20.1.24
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    Citations

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    Cited by:

    1. Xiao Fu & William Lam, 2014. "A network equilibrium approach for modelling activity-travel pattern scheduling problems in multi-modal transit networks with uncertainty," Transportation, Springer, vol. 41(1), pages 37-55, January.
    2. Vance, Colin & Procher, Vivien, 2013. "Who Does the Shopping? German time-use evidence, 1996-2009," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 2357, pages 125-133.
    3. Xiao Fu & William H. K. Lam, 2018. "Modelling joint activity-travel pattern scheduling problem in multi-modal transit networks," Transportation, Springer, vol. 45(1), pages 23-49, January.
    4. Arentze, Theo A. & Ettema, Dick & Timmermans, Harry J.P., 2011. "Estimating a model of dynamic activity generation based on one-day observations: Method and results," Transportation Research Part B: Methodological, Elsevier, vol. 45(2), pages 447-460, February.
    5. Marcela Munizaga & Sergio Jara-Díaz & Javiera Olguín & Jorge Rivera, 2011. "Generating twins to build weekly time use data from multiple single day OD surveys," Transportation, Springer, vol. 38(3), pages 511-524, May.
    6. Kang, Hejun & Scott, Darren M., 2010. "Exploring day-to-day variability in time use for household members," Transportation Research Part A: Policy and Practice, Elsevier, vol. 44(8), pages 609-619, October.
    7. Wang, Donggen & Cao, Xinyu, 2017. "Impacts of the built environment on activity-travel behavior: Are there differences between public and private housing residents in Hong Kong?," Transportation Research Part A: Policy and Practice, Elsevier, vol. 103(C), pages 25-35.

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