Nonparametric Method to Examine Changes in Traffic Volume Pattern during Holiday Periods
Bibliographic record
Abstract
Rising standards of living and the trend toward shorter working weeks have led to significant changes in traffic volume during holiday periods. The challenge of holiday traffic has been reported worldwide in the literature. A comprehensive understanding of the changes in traffic volume pattern associated with holiday events is critical to many transportation engineering aspects such as traffic control; signal timing; road safety; and traffic volume monitoring, imputation, and prediction. However, existing research focusing on holiday traffic has been very limited to date, and the efforts made have generally been limited to a particular holiday period or a specific recreational area. A nonparametric hypothesis test method was used to examine the changes in traffic volume patterns caused by holidays. The data used in this study were traffic volumes collected from Canadian highway networks over the past 20 years. To illustrate the application and effectiveness of the proposed method, in-depth investigations were carried out with respect to the variation of both weekly and daily volumes during 12 Canadian holiday periods. The test results showed that, for various types of roads, this method is effective and easy to understand. Moreover, the outputs were informative and reasonable. This method also may be a useful tool to examine the variation characteristics of traffic during other special time periods.
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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 | 0.007 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".