Research on the Prepositive Distance of Crosswalk Warning Markings for Unsignalized Road Section
Bibliographic record
Abstract
This paper proposed an optimal prepositive distance of crosswalk warning markings for unsignalized road section under three different design speeds based on the mathematical modelling and driving simulation. To set up the most efficient mathematical modelling for calculating the layout interval of prepositive distance, the vehicles slowing down behaviour characteristics in front of crosswalk were explored. According to the layout interval, the simulation experiment was carried out in the UC-win/Road version 13.0 driving simulator. The rate of speed reduction and the times of maximum deceleration obtained from simulation experiments were selected as evaluation indicators to compare and analyse the deceleration effect related with the prepositive distances of the crosswalk warning markings under three design speeds. The results show that when the design speeds are 30 km/h, 40 km/h, and 50 km/h, the optimal prepositive distances of the crosswalk warning markings are 30 m, 40 m, and 60 m, respectively.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 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".