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Longitudinal analysis of activity generation in the Greater Toronto and Hamilton Area

Author

Listed:
  • Gozde Ozonder

    (Universiy of Toronto)

  • Eric J. Miller

    (Universiy of Toronto)

Abstract

This paper presents a longitudinal analysis of activity generation behaviour in the Greater Toronto and Hamilton Area (GTHA) between 1996 and 2016 for various activity types: work, school, shopping, other. The analyses are conducted using the data from the five most recent Transportation Tomorrow Surveys. For work and school purposes, the population is divided into sub-categories considering occupational sectors and educational levels respectively. Further subdivision is made by treating first work/school activity of the day and subsequent work/school activities as distinct activity types. Considerable stability over time in the majority of the model parameters is found in all cases, indicating that both work/school and non-work/school activity episode generation in the GTHA has been very stable over the 20-year period analyzed. Year-specific models and joint models, within which the data are pooled across the years, return very similar results implying that robust joint models that exploit the full time-series of survey data available can be constructed. While first-trips to work and post-secondary schools in the day can be parametrically modelled with reasonable fits, second/subsequent work/school activities and non-work/school activities display considerable randomness in occurrence. Elementary and secondary school trips generally need only be modelled using average trip rates across the student population: parametric, utility-based models provide very little additional explanatory power. In addition, investigation of survey design biases shows that there is no significant survey design effect on activity/trip generation for the first work/school-related activities, however, the models reveal significant biases when the subsequent work/school-related activities and non-work/school activities are analyzed.

Suggested Citation

  • Gozde Ozonder & Eric J. Miller, 2021. "Longitudinal analysis of activity generation in the Greater Toronto and Hamilton Area," Transportation, Springer, vol. 48(3), pages 1149-1183, June.
    • Handle: RePEc:kap:transp:v:48:y:2021:i:3:d:10.1007_s11116-020-10089-w
    DOI: 10.1007/s11116-020-10089-w
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    References listed on IDEAS

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    1. Cameron,A. Colin & Trivedi,Pravin K., 2013. "Regression Analysis of Count Data," Cambridge Books, Cambridge University Press, number 9781107667273, January.
    2. Farag, Sendy & Schwanen, Tim & Dijst, Martin & Faber, Jan, 2007. "Shopping online and/or in-store? A structural equation model of the relationships between e-shopping and in-store shopping," Transportation Research Part A: Policy and Practice, Elsevier, vol. 41(2), pages 125-141, February.
    3. Zeileis, Achim & Kleiber, Christian & Jackman, Simon, 2008. "Regression Models for Count Data in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 27(i08).
    4. Jesse W.J. Weltevreden & Ton Van Rietbergen, 2007. "E‐Shopping Versus City Centre Shopping: The Role Of Perceived City Centre Attractiveness," Tijdschrift voor Economische en Sociale Geografie, Royal Dutch Geographical Society KNAG, vol. 98(1), pages 68-85, February.
    5. Mullahy, John, 1986. "Specification and testing of some modified count data models," Journal of Econometrics, Elsevier, vol. 33(3), pages 341-365, December.
    6. Golob, Thomas F. & Regan, Amelia C., 2001. "Impacts of Information Technology on Personal Tavel and Commercial Vehicle Operations: Research Challenges and Opportunities," University of California Transportation Center, Working Papers qt95r7j7vk, University of California Transportation Center.
    7. Train,Kenneth E., 2009. "Discrete Choice Methods with Simulation," Cambridge Books, Cambridge University Press, number 9780521747387.
    8. Eric Miller & Matthew Roorda & Juan Carrasco, 2005. "A tour-based model of travel mode choice," Transportation, Springer, vol. 32(4), pages 399-422, July.
    9. Christian Kleiber & Achim Zeileis, 2016. "Visualizing Count Data Regressions Using Rootograms," The American Statistician, Taylor & Francis Journals, vol. 70(3), pages 296-303, July.
    10. Esra Suel & John W. Polak, 2018. "Incorporating online shopping into travel demand modelling: challenges, progress, and opportunities," Transport Reviews, Taylor & Francis Journals, vol. 38(5), pages 576-601, September.
    11. Orit Rotem-Mindali & Jesse Weltevreden, 2013. "Transport effects of e-commerce: what can be learned after years of research?," Transportation, Springer, vol. 40(5), pages 867-885, September.
    12. Farhana Yasmin & Catherine Morency & Matthew J. Roorda, 2017. "Trend analysis of activity generation attributes over time," Transportation, Springer, vol. 44(1), pages 69-89, January.
    13. Arne Henningsen & Ott Toomet, 2011. "maxLik: A package for maximum likelihood estimation in R," Computational Statistics, Springer, vol. 26(3), pages 443-458, September.
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