Statistical Investigations of Statutory Holiday Effects on Traffic Volumes
Why this work is in the frame
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Bibliographic record
Abstract
Traffic volume fluctuates from time to time and from location to location, with significant variations in demand as a result. The increases in travel during statutory holiday periods are substantial, and some critical traffic problems have been reported. An understanding of this substantial variation in the volume of traffic can assist transportation agencies in developing practical countermeasures in aspects such as traffic control plans, signal timing, safety programs, traffic volume monitoring, and prediction. The literature on holiday traffic is limited, and no effort has been made to examine statistically the significance of changes in traffic volume due to holiday effects. With the past 20 years of data collected by permanent traffic counters on highways in Alberta, Canada, holiday effects on road traffic are shown graphically. Then, the nonparametric Wilcoxon matched pair test is used to test the variation characteristics of normal flow, the Friedman method is applied to investigate the holiday effects on weekly and daily traffic, and the hourly volume pattern changes are examined by a combination of χ2 and binomial tests. The test results reveal that holidays substantially contribute to the variability of traffic. The weekly volume variations during holiday periods are significant in many cases, holiday effects on daily and hourly traffic are evident, and the directional holiday traffic peaking features are strong. Meanwhile, general identifications of the affected holidays for different types of roads are provided. Potential implications of these findings are discussed.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it