Vertical and Horizontal Queue Models for Oversaturated Signal Intersections with Quasi-Real-Time Reconstruction of Deterministic and Shockwave Queueing Profiles Using Limited Mobile Sensing Data
Bibliographic record
Abstract
Deterministic/point/vertical and shockwave/physical/horizontal queueing models are widely used in traffic operation to estimate vehicular queue length and delays at bottlenecks such as signalized intersections. The consistency between the two types of queueing model in terms of their estimation performance has been a subject of debate for decades. This paper reexamines the issue, typically with respect to oversaturated signal intersections, and demonstrates the consistency based on analytical studies and microscopic simulations. While fixed-location sensor data was dominating, it was hardly possible to construct the deterministic or shockwave queueing profile using real data. For this reason, either profile had significance only at a conceptual level and could not be put into practical usage. With the quick spread of mobile sensing data, however, the situation has drastically changed. In this context, this paper also intends to develop an efficient approach to the reconstruction of the deterministic and shockwave queueing profiles in a quasi-real-time manner using very limited mobile sensing data. Microscopic simulations with AIMSUN have demonstrated the efficiency of the approach as well as the analytical results obtained in this paper.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".