Evolution of Canadian time-use patterns: Multidimensional activity-based approach
Bibliographic record
Abstract
The review of recent research efforts in travel demand modeling community clearly indicates a paradigm shift from an aggregate trip based statistical modeling approach towards a more disaggregate activity-based behavioural modeling approach. The dissertation contributes to transportation literature by advancing the state of the art in multiple discrete continuous models. Also, the dissertation research aims to contribute substantively to transportation literature by undertaking a first of its kind analysis the evolution of Canadian activity participation patterns using 4 waves of cross-sectional time-use data from the Canadian General Social Survey (GSS). I started my study by proposing an innovative form of the Multiple Discrete Extreme Value (MDCEV) model (Chapter 3). The Latent MDCEV model accounts for the population's heterogeneity. Also, a prediction procedure to employ the estimated Latent MDCEV models for forecasting is developed. The results of the model estimates and prediction exercises illustrate the benefits of employing an endogenous segmentation based MDCEV model. Then in the next chapter (Chapter 4), I examined the weekday time-use patterns of Canadians aged 20 years or older four waves of data from GSS compiled for the years 1992, 1998, 2005 and 2010. The results provide evidence that the proposed approach provides (Scaled MDCEV) an appropriate framework to study activity participation decision process evolution in time. The next subject (Chapter 5) examines the evolution patterns of senior's weekday daily activity participation decisions. The GSS database is used to analyze the development of activity participation decisions throughout 13 years (1998 to 2010). New habits have emerged following the information and technology revolution that have changed the way we communicate, interact, and make decisions; therefore it is clearly important to study the trend of leisure activity participation and more specifically, the potential observed and unobserved impact of Information and Communication Technologies (ICT) on adults' participating in this activity group as presented in Chapter 6. The econometric framework applied to the GSS dataset was the Ordered Regression Duration (ORD) model. The final chapter of this dissertation (Chapter 7) proposes recommendations and suggestions based on the obtained model results from all chapters. Study limitations are also presented along with future work as a continuation of this dissertation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.012 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".