Multivariate calibration of single regime speed-flow-density relationships [road traffic management]
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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