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Modelo para avaliação do desempenho da segurança viária através da simulação microscópica

2010· article· pt· W1582842212 on OpenAlexaff
Flávio José Craveiro Cunto, Frank Saccomanno

Bibliographic record

VenueTransportes · 2010
Typearticle
Languagept
FieldEngineering
TopicTraffic and Road Safety
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsTransport engineeringEngineering

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.007
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.022
GPT teacher head0.255
Teacher spread0.233 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreMethods

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".

Quick stats

Citations0
Published2010
Admission routes1
Has abstractyes

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