Transcending the Typical Weekday with Large-Scale Single-Day Survey Samples
Bibliographic record
Abstract
Many urban areas are increasingly experiencing significant levels of congestion where and at times when travel was once easy. In fact, the norm today is heavy congestion during off-peak periods and at weekends. However, most models and planning frameworks rely on data for the a.m. peak period for a typical weekday, and important decisions are based on their results. A better understanding of the changes occurring in metropolitan areas is required to assist in decision making, and at the same time the evolution of the data and methods used must be continuously monitored. In such a context, it is important to ask questions about specific travel behaviors on weekdays and weekend days and about how the behaviors vary throughout the year. Therefore, the concept of a typical weekday needs to be challenged for modeling purposes and updated to consist of a concept that will more thoroughly represent the complexity of travel and activity behaviors. With the benefit of the availability of the large-scale origin–destination travel surveys regularly carried out in the greater Montreal, Canada, area, this paper illustrates how such data can help assess the variability of behaviors. By using critical indicators of travel behaviors and statistical methods, this research confirms that behavior differs significantly across the days of the week.
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 | 0.026 | 0.113 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".