Computerized Household Activity-Scheduling Survey for Toronto, Canada, Area: Design and Assessment
Bibliographic record
Abstract
Traditional activity-travel diary surveys have for some time served as the primary source of data for understanding and modeling travel behavior. Recent changes in policy and forecasting needs have led to the development of an emerging class of activity-scheduling process surveys that focus on the underlying behavioral mechanisms that give rise to travel and condition future change. Many of these surveys involve the use of computers for data entry over multiday periods. These changes pose new challenges and opportunities for quality assessment. At this early stage it is more important than ever to document closely the quantity and quality of data provided by such surveys as well as the associated burden and experience of respondents. This study reviews existing quality standards and seeks to develop several new data-quality measures suitable to this emerging class of surveys. Data are used from a recent household activity-scheduling survey of 271 households in Toronto, Ontario, Canada. A detailed description of the survey instrument is provided, along with an in-depth examination of key results that shed light on data quality. Included are results from a separate survey of 31 respondents concerning their experiences and perceptions of the survey. Overall, although the survey was generally successful in tracking both observed patterns and underlying decision processes over a multiday period within a household, it did come at a price in terms of respondent burden. On the basis of these results several new data-quality guidelines are suggested that incorporate use of activity-trip rates, scheduling-step rates, planning-time horizons, and log-in durations. Further specific suggestions for reducing respondent burden are suggested.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.011 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".