Developing Real-Time Queue Estimation Model with Dynamic Capacity based on Shock Wave Analysis
Bibliographic record
Abstract
The authors developed a time-space discrete macroscopic model based on the shockwave theory for real-time queue estimation at active bottlenecks. In the proposed model, the authors consider dynamic capacity, because, when queued, vehicles may not be stationary. With different demand inputs in the same bottleneck, the authors found that, from queue onset, the discharge flow was dynamic; this was the most sensitive parameter influencing the accuracy of queue-length estimation. The authors determined queue onset time, and investigated several bottlenecks on an urban freeway in Edmonton, Canada, and estimated the input parameters from loop detector data. The authors compared the real-time queue length (obtained from VISSIM 5.3) with the proposed macroscopic model, which included the dynamic parameters, and the base microscopic model, which excluded the dynamic parameters. The queue analysis was done using a shockwave, VISSIM-simulated scenario, functioning as the real world traffic system. The proposed model more accurately estimated queue length than the base model.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".