Multivariate calibration of single regime speed-flow-density relationships [road traffic management]
Bibliographic record
Abstract
This paper presents a multivariate procedure for performing automated fitting of speed-flow relationships for different roads based on loop detector data. The procedure is shown to fit the observations for different freeway, tunnel and arterial data, thus demonstrating its flexibility in terms of representing different types of roads. Furthermore, the procedure also provides a fit that is reasonable for all data regimes, unlike many other single regime models that only fit free-flow or forced flow conditions data. Finally, this single-regime model provides a quality of fit that is consistent with most multi-regime models, without the need to deal with the complexities associated with the selection of regime break points. In addition to demonstrating the fit of the model to well known sample data from a standard traffic flow text books, fits to three different recent data sets with 1 to 5 minute loop detector data are also presented. These fits demonstrate that the flexibility of the proposed technique to deal with real-time data for both Europe and North America.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| 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".