Weekly Rhythm in Joint Time Expenditure for All At-Home and Out-of-Home Activities
Bibliographic record
Abstract
This paper uses the Kuhn-Tucker demand system modeling technique to investigate the capacity of a typical week in capturing rhythms in activity-travel behavior. It considers all possible activity types within a weeklong modeling time frame. Complex interactions in time expenditure between at-home and out-of-home activities and among different out-of-home activities are captured by introducing behavioral elements in the model in terms of baseline preference, time translation, and satiation effects. The Kuhn-Tucker demand system model used in this paper is a random utility maximization model with the inherent assumption that every individual maximizes total utility in allocating time to the activities under consideration within the modeling time frame. Models are developed for each individual week of a 6-week travel diary drawn from the MobiDrive data set for Karlsruhe and Halle, Germany. Each model contains 83 variables and reveals behavioral details of complex activity-travel behavior. Based on the performances of the models in terms of fitting observed data and parameter values of specific variables, it is clear that a modeling time frame for a typical week is capable of capturing the rhythms of activity-travel behavior sufficiently. The paper concludes with the recommendation that the availability of activity diary data for a multiweek time period would further enhance understanding on this issue.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".