Real-time queue estimation model development for uninterrupted freeway flow based on shockwave analysis
Bibliographic record
Abstract
In this study, the authors developed a time-space discrete macroscopic model based on the shockwave theory for real-time queue estimation in uninterrupted freeway flow, using fixed-location loop detector data. After investigating the queue characteristics both at an active bottleneck and within a variable speed limit control case, the proposed model was applied to these two cases on Whitemud Drive, a major freeway corridor in Edmonton, Alberta, Canada. Modified Highway Capacity Manual–based methods were used to determine queue density in uninterrupted freeway flow. The effect of time interval size on queue estimation was studied, as loop detector data acquisition frequencies may differ. It was found that the proposed model accurately estimates real-time queue length independent of the time interval. Multiple single queues were implemented in a calibrated VISSIM 5.3 micro-simulation model to perform the validation task. The study is a helpful foundation for future active traffic management strategy development and improvement.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".