1 Travel/Activity Panel Surveys in the Toronto and Quebec City Regions: Comparison of Methods and Preliminary Results
Bibliographic record
Abstract
Recent developments in activity-based modelling have underscored the need for data collection techniques that allow researchers to observe the activity scheduling process as directly as possible. Qualitative and quantitative observations are needed to improve our understanding of the factors that influence activity / travel behaviour, to help inform model structure, and to form the basis for formulating behavioural rule sets within the models. To address this need, in-depth longitudinal surveys are being conducted on panels of participants in two Canadian urban areas: Toronto and Quebec City. A total of 520 responding households from the two areas conduct in-depth surveys at one-year intervals. The panel surveys use a variety of instruments, each emphasizing different, but complementary, aspects of the scheduling process. This paper describes the overall data collection philosophy and the detailed design of the instruments used in each wave in Toronto and Quebec City. A discussion of the advantages and challenges associated with each instrument and a preliminary comparison of results for the first two waves of the surveys in each region are also provided.
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".