Studying Differences of Household Weekday and Weekend Activities
Bibliographic record
Abstract
A desired activity-based travel demand modeling framework should be able to address both weekday and weekend activities. However, a literature review shows that previous research efforts have mostly focused on weekday, not weekend, activities, and that little or no research exists to quantify the differences between the two. The best knowledge to date is limited to weekday and weekend activities that start at different times of the day and have different participation rates. This paper aims to fill the gap by studying the differences between weekday and weekend activities in Calgary, Canada, in terms of participation rates, starting times, duration, and inferred location choices. First, statistics related to these attributes were computed for 10 types of weekday and weekend activities (these were found to differ). Second, log-rank and Wilcoxon tests were used to prove further that common types of weekday and weekend activities tend to follow different survival functions. Third, best-fit duration models were explored for each type of weekday and weekend activity and compared with each other. It was found that Weibull and log-normal were chosen as the best-fit models for nearly all weekday and weekend activities. The best-fit duration models for the same types of weekday and weekend activities (e.g., shopping) were found to be different in either underlying distribution or estimated parameters. This study clearly shows that the weekend activities differ from their weekday counterparts and suggests that they be treated separately in activity-based modeling frameworks.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".