Improving Group Assiginment and AADT Estimation Accuracy of Short-Term Traffic Counts Using Historical Seasonal Patterns
Bibliographic record
Abstract
The importance of reliable estimates of travel demands for effective planning, design, and management of roads and facilities is well known by transportation engineers. The Federal Highway Administration (FHWA) factoring method is widely used by highway agencies to estimate AADTs for a wide range of roads, which are not possible to be covered by the permanent counters. This approach assumes that roads within a functional class have similar traffic variations and thus factors derived from the class can be applied to short-term traffic counts (STTCs) to account for seasonal variations, which have been shown to produce large AADT estimation errors sometimes in the literature. This paper contributes to improving the AADT estimations by constructing seasonal traffic variation profiles using all historical short-term counts available without imposing any additional monitoring cost, or making any change to existing traffic monitoring programs. Two pattern matching methods are proposed and tested with the data from a permanent counter on a winter recreational road in Alberta, Canada. It is found that the resulting 95th percentile (P95) AADT estimation errors of the two methods are 10.5% and 8.4% respectively, with a contrast to 62.1% from the FHWA method.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".