Evaluation of Proneness to Red Light Violation: Quantitative Approach Suggested by Potential Conflict Analysis
Bibliographic record
Abstract
Evaluation of proneness to red light running behavior, because it results from human and road factors, can aid in a proper selection of the sites to be treated and thereby increase the benefit of countermeasures to reduce the red light running (RLR) phenomenon. Starting from the conceptual framework of a model based on potential conflicts analysis, this paper shows that a quantitative evaluation of proneness to red light running behavior can be obtained from both the analysis of the effective operational characteristics of the intersection and the actual number of RLR violations. According to behavioral models referred to in the literature, which emphasize the influence that both human and road factors have on the user's decision-making process at red lights, the proposed approach also accounts for the impact of the local (site) and general (population) characteristics on the phenomenon. Field observations for a case study in an urban area are discussed to illustrate the methodological approach. Results obtained clearly show the sensitivity of the parameter assumed for describing proneness to red light violation to the operational characteristics of the intersections. Moreover, they underline that the common violation rates could cause improper evaluation of the extent of the RLR phenomenon at a specific intersection.
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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 | 0.018 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.003 |
| 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".