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Record W2044809403 · doi:10.1139/l09-092

Simulated safety performance of rear-end and angled vehicle interactions at isolated intersections

2009· article· en· W2044809403 on OpenAlexaffvenue
Flávio José Craveiro Cunto, Frank Saccomanno

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2009
Typearticle
Languageen
FieldEngineering
TopicTraffic control and management
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsIntersection (aeronautics)PercentileAutomotive engineeringTraffic volumeEngineeringTransport engineeringSimulationStatisticsMathematics

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.070
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.004
GPT teacher head0.164
Teacher spread0.161 · 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 teacher head, not a consensus.

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

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

Citations14
Published2009
Admission routes2
Has abstractyes

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