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

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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.

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Notes

  1. Exceptions include facilitating a passenger or taking children to daycare.

  2. Since the activity participation frequencies are non-negative integers, ordinary least squares regression is not a suitable model structure as it does not restrict the dependent variable to be non-negative (Susmel 2015).

  3. There can only be one individual from each household who is designated as the respondent, as the survey is completed through a single person, unless the survey is completed online in year 2016.

  4. All parameter estimates are statistically significant at least at a 95% confidence interval and are of expected signs. Parameter names are self-explanatory.

  5. Owing to large sample sizes, adjusted ρ2 values in all models are almost equal to ρ2 values, hence they are not provided here.

  6. Note that, insignificant variables were removed from the second part of the count data models after exploring all possible regressors.

  7. Individual’s age should be greater than or equal to 11 to be considered as “TRE”. This definition follows from the fact that TTS does not record the trips of individuals younger than 11.

  8. “PD” stands for “planning district”.

  9. This is expected as they are almost the same model in the given particular case.

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Acknowledgements

This study was funded in part by an Ontario Graduate Scholarship and a Richard M. Soberman Fellowship (Department of Civil and Mineral Engineering, University of Toronto). Partial funding was also provided by a Natural Sciences and Engineering Research Council (Canada) Discovery Grant (RGPIN-2014-04479).

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  1. Department of Civil and Mineral Engineering, Universiy of Toronto, Toronto, M5S 1A4, Canada

    Gozde Ozonder & Eric J. Miller

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GO: Analysis, literature review, manuscript writing, editing and content planning, EJM: Supervision on analysis and results, manuscript writing, editing and content planning.

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Ozonder, G., Miller, E.J. Longitudinal analysis of activity generation in the Greater Toronto and Hamilton Area. Transportation 48, 1149–1183 (2021). https://doi.org/10.1007/s11116-020-10089-w

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