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Estimating a model of dynamic activity generation based on one-day observations: Method and results

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  • Arentze, Theo A.
  • Ettema, Dick
  • Timmermans, Harry J.P.

Abstract

In this paper we develop and explore an approach to estimate dynamic models of activity generation on one-day travel-diary data. Dynamic models predict multi-day activity patterns of individuals taking into account dynamic needs as well as day-varying preferences and time-budgets. We formulate an ordered-logit model of dynamic activity-agenda-formation decisions and show how one-day observation probabilities can be derived from the model as a function of the model's parameters and, with that, how parameters can be estimated using standard loglikelihood estimation. A scale parameter cannot be identified because information on within-person variability is lacking in one-day data. An application of the method to data from a national travel survey illustrates the method. A test on simulated data indicates that, given a pre-set scale, the parameters can be identified and that estimates are robust for a source of heterogeneity not captured in the model. This result indicates that dynamic activity-based models of the kind considered here can be estimated from data that are less costly to collect and that support the large sample sizes typically required for travel-demand modeling. We conclude therefore that the proposed approach opens up a way to develop large-scale dynamic activity-based models of travel demand.

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  • 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.
    • Handle: RePEc:eee:transb:v:45:y:2011:i:2:p:447-460
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    References listed on IDEAS

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    2. Allahviranloo, Mahdieh & Recker, Will, 2013. "Daily activity pattern recognition by using support vector machines with multiple classes," Transportation Research Part B: Methodological, Elsevier, vol. 58(C), pages 16-43.
    3. Maya Abou-Zeid & Moshe Ben-Akiva, 2012. "Well-being and activity-based models," Transportation, Springer, vol. 39(6), pages 1189-1207, November.
    4. Linda Nijland & Theo Arentze & Harry Timmermans, 2013. "Representing and estimating interactions between activities in a need-based model of activity generation," Transportation, Springer, vol. 40(2), pages 413-430, February.
    5. Nursitihazlin Ahmad Termida & Yusak O. Susilo & Joel P. Franklin, 2016. "Examining the effects of out-of-home and in-home constraints on leisure activity participation in different seasons of the year," Transportation, Springer, vol. 43(6), pages 997-1021, November.
    6. 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).
    7. Han, Qi & Arentze, Theo & Timmermans, Harry & Janssens, Davy & Wets, Geert, 2011. "The effects of social networks on choice set dynamics: Results of numerical simulations using an agent-based approach," Transportation Research Part A: Policy and Practice, Elsevier, vol. 45(4), pages 310-322, May.
    8. Pougala, Janody & Hillel, Tim & Bierlaire, Michel, 2022. "Capturing trade-offs between daily scheduling choices," Journal of choice modelling, Elsevier, vol. 43(C).

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