Modeling the Traffic Flow of the Urban Signalized Intersection with a Straddling Work Zone
Bibliographic record
Abstract
Work zones widely exist on urban roads in many countries and have a significant negative impact on traffic. Few studies have focused on modeling the traffic flow of the work zone on the urban arterials, especially on the work zone at the intersections. In this paper, a microscopic model based on the social force theory for the traffic flow of the intersection with a specific work zone, called straddling work zone, is proposed. The model can capture the no lane division and irregular boundary characteristics of the traffic of the intersection with a straddling work zone and also can reflect the interaction of the intersection traffic flows from the two opposite directions. The proposed model is calibrated and validated using the real work zone data, and the results display that the MARE values are all less than 10%. The factors affecting the traffic flow in the straddling work zone are analyzed through simulation. Our study reveals that the distance from the lower edge of the work zone to the median divider of the road and the proportion of large vehicles in the work zone have the greatest impact on the signalized intersection, which provides a reference for the future traffic control at the intersection with the straddling work zone.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".