Modelo para avaliação do desempenho da segurança viária através da simulação microscópica
Bibliographic record
Abstract
Resumo: O uso da microssimulação em estudos de segurança viária tem sido investigado mais frequentemente nas últimas duas décadas.Em tese, essa ferramenta pode atuar como plataforma para o desenvolvimento de uma abordagem mais mecanística dos eventos que precedem a ocorrência de acidentes de trânsito. Este artigo apresenta um modelo para avaliação do desempenho da segurança viária através da microssimulação. O modelo utiliza o aplicativo VISSIM© versão 4.3 como plataforma de simulação e estima interações longitudinais e transversais entre veículos ao longo do tempo, a partir do índice de potencial para acidentes (CPI). A utilidade do modelo proposto foi ilustrada através de sua aplicação em interseções isoladas semaforizadas ou não. Os resultados indicam que a introdução do semáforo aumentou a frequência e severidade das interações longitudinais e, reduziu o número de veículos interagindo transversalmente. Estes resultados confirmam o potencial considerável para o uso da microssimulação em estudos de segurança viária.Abstract: The use of microsimulation in safety studies has been more frequently investigated over the last two decades. In theory, this tool can serve as platform for the development of a more mechanistic approach regarding the events preceding a crash. This paper presents a model for assessing the road safety performance using microsimulation. The model applies the software VISSIM© 4.3 as simulation platform and estimates rear-end and angled interactions for different vehicle over time via the crash potential index (CPI). The usefulness of the proposed model has been illustrated throughout its application to signalized and unsignalized isolated intersections. The results indicate that the signalization increased both frequency and severity for rear-end interactions, decreasing, on the other hand, the number of angled interactions. These results also confirm the potential for using microscopic simulation in road safety studies.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".