Analysis of Routine Weekly Activity/Travel Patterns
Bibliographic record
Abstract
As part of Wave 3 (2005-2006) of the Travel/Activity Panel Survey (TAPS) in Toronto, 300 respondents in 145 households described the detail of their routine weekly activities, including information concerning routine activity episodes’ purposes and usual durations, start times (by day of the week), locations and travel modes. Information concerning the degree of flexibility in these routine episode activities was also collected. This paper first describes the survey instrument used to collect this information. This involved respondents marking on a large sheet of paper their routine activities by time of day and day of the week, using a combination of free-form descriptions and pre-specified symbols. The paper then presents a detailed analysis of the survey data collected. Included in the analysis is a comparison between routine activities as reported prior to the survey week and those activities as they were actually executed, as recorded in a two-day activity diary, which was also part of the Wave 3 survey. This analysis shows that the notion that the “skeleton schedule” consists only of activities that are deterministically defined by their activity type (e.g. “mandatory” activities) and augmented with “flexible” household maintenance and discretionary activities is unfounded. Many exceptions are found to this rule. Men are found to have a greater proportion of routine activities than women, with the exception of weekday work/school activities. The analysis suggests that it may be appropriate to model schedule building in layers, with fully routine non-flexible activities (of all activity types) scheduled first, routine but flexible activities scheduled second, and non-routine activities scheduled last.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".