Predicting Directional Design Hourly Volume from Statutory Holiday Traffic
Bibliographic record
Abstract
Estimating design hourly volume (DHV)—commonly the 30th highest hourly volume (30HV) in a year—from sample counts is an important aspect of traffic engineering practice. Directional DHV (DDHV) on highways without permanent traffic counters (PTCs) is usually determined by the estimated annual average daily traffic (AADT) being multiplied by the ratio of DHV to AADT and the directional split ratio during the DHV at a PTC on a similar road. However, highway designers have questioned the validity of this method, and its limitations have been well discussed. Because recreational travel on most holidays in developed countries is active and increases highway traffic volumes vastly, the main intent is to develop more accurate and efficient DDHV prediction models based on directional hourly volumes that occur during holiday periods. The existing literature on DHV is reviewed, then holiday traffic peaking characteristics are investigated on the basis of the past 20 years of data from PTCs on rural highways in Alberta, Canada. Accounting for holiday traffic peaking characteristics such as directional peaking features, discernible and consistent hourly volume patterns during holiday weeks, and the remarkable contributions of holiday travel to the yearly highest hourly volumes, genetic algorithms (GAs) are used to assist in the development of several DDHV prediction models that correspond to various holidays and road types. The analysis results indicate that GA-assisted DDHV models consistently outperform the existing models.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".