Simulated safety performance of rear-end and angled vehicle interactions at isolated intersections
Bibliographic record
Abstract
This paper applies a calibrated microscopic simulation model to assess the safety implications of signalization at a stop-controlled isolated intersection. Safety performance is measured in terms of a crash potential index (CPI) that makes use of time-specific vehicle parameters, such as deceleration rates, spacing, and speed profiles. Four performance measures are obtained: (i) average CPI/vehicle, (ii) CPI 85th percentile, (iii) number of vehicles with CPI > 0 (defined as interacting), and (iv) number of conflicts (defined in terms of a given CPI threshold). Two types of interactions are considered, namely rear end and angled. For rear-end interactions, CPI/vehicle was found to be significantly higher following the introduction of fixed signal controls. For angled interactions, CPI/vehicle was found to decrease with signalization. For both types of interactions, the CPI 85th percentile was found to decrease nonlinearly with signalization, especially for higher assumed volumes on the major approach. Rear-end vehicle interactions increased significantly following signalization and with increasing volume, whereas no such increase was observed for angled interactions. The key observation is that the number of vehicles subject to angled interactions was found to decrease after signalization.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.002 | 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".