Investigating the Capacity of Continuous Household Travel Surveys in Replacing Traditional Cross-sectional Surveys
Bibliographic record
Abstract
In this thesis, the capacity of continuous surveys in replacing cross-sectional surveys is examined. A flexible framework for both cross-sectional and continuous household travel survey sample size determination is proposed. After that, the state of practice of continuous surveys is closely examined. It is believed that the main advantage of continuous surveys is the availability of data over a continuous spectrum of time. This claim is put to the test by estimating mixed effects models on different levels using the Montreal Continuous Survey data. The use of the mixed effects econometric framework allows for partitioning the variance of the dependent variable to a set of grouping factors, such as time periods and spatial units, enabling the understanding of the underlying causes of variation in travel behavior. The thesis concludes that the temporal variability in trip behavior is only observed when modelling on the regional or modal level.
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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 | 0.202 | 0.590 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.011 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".