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Analysis of activity duration using the Puget sound transportation panel

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  • Yee, Julie L.
  • Niemeier, Debbie A.

Abstract

In this paper, we extend previous analyses of temporal effects by examining the factors associated with activity generation and duration over four waves of the Puget sound transportation panel survey (PSTPS). A Cox proportional hazards model was specified for each of five activities: visiting, appointment, free time, personal business, and shopping. For each activity, the duration times are modeled with an emphasis on examining important higher order interactions. The results suggest that activity durations have changed significantly over the survey period. Many of the differences in activity durations over time were significant, and often associated with increasing numbers of children in the household and higher order interactions between sex and the sequencing of activities.

Suggested Citation

  • Yee, Julie L. & Niemeier, Debbie A., 2000. "Analysis of activity duration using the Puget sound transportation panel," Transportation Research Part A: Policy and Practice, Elsevier, vol. 34(8), pages 607-624, November.
    • Handle: RePEc:eee:transa:v:34:y:2000:i:8:p:607-624
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    References listed on IDEAS

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    1. Heckman, James & Singer, Burton, 1984. "A Method for Minimizing the Impact of Distributional Assumptions in Econometric Models for Duration Data," Econometrica, Econometric Society, vol. 52(2), pages 271-320, March.
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    3. Bhat, Chandra R., 1996. "A hazard-based duration model of shopping activity with nonparametric baseline specification and nonparametric control for unobserved heterogeneity," Transportation Research Part B: Methodological, Elsevier, vol. 30(3), pages 189-207, June.
    4. Han, Aaron & Hausman, Jerry A, 1990. "Flexible Parametric Estimation of Duration and Competing Risk Models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 5(1), pages 1-28, January-M.
    5. Bhat, Chandra R., 1996. "A generalized multiple durations proportional hazard model with an application to activity behavior during the evening work-to-home commute," Transportation Research Part B: Methodological, Elsevier, vol. 30(6), pages 465-480, December.
    6. White, Halbert, 1982. "Maximum Likelihood Estimation of Misspecified Models," Econometrica, Econometric Society, vol. 50(1), pages 1-25, January.
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    Cited by:

    1. Iragaël Joly & Karl Littlejohn & Vincent Kaufmann, 2006. "La croissance des budgets-temps de transport en question : nouvelles approches," Post-Print halshs-00174992, HAL.
    2. Jason D. Lemp & Kara M. Kockelman & Paul Damien, 2012. "A Bivariate Multinomial Probit Model for Trip Scheduling: Bayesian Analysis of the Work Tour," Transportation Science, INFORMS, vol. 46(3), pages 405-424, August.
    3. Stephan Brunow & Manuela Gründer, 2013. "The impact of activity chaining on the duration of daily activities," Transportation, Springer, vol. 40(5), pages 981-1001, September.
    4. Lee, Backjin & Timmermans, Harry J.P., 2007. "A latent class accelerated hazard model of activity episode durations," Transportation Research Part B: Methodological, Elsevier, vol. 41(4), pages 426-447, May.
    5. Weis, Claude & Axhausen, Kay W., 2009. "Induced travel demand: Evidence from a pseudo panel data based structural equations model," Research in Transportation Economics, Elsevier, vol. 25(1), pages 8-18.
    6. Chunguang Liu & Xinyu Zuo & Xiaoning Gu & Mengru Shao & Chao Chen, 2023. "Activity Duration under the COVID-19 Pandemic: A Comparative Analysis among Different Urbanized Areas Using a Hazard-Based Duration Model," Sustainability, MDPI, vol. 15(12), pages 1-28, June.

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