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Record W2057889541 · doi:10.3141/1746-06

Simulation of Road Crashes by Use of Systems Dynamics

2001· article· en· W2057889541 on OpenAlexaff
Arif Mehmood, Frank Saccomanno, Bruce Hellinga

Bibliographic record

VenueTransportation Research Record Journal of the Transportation Research Board · 2001
Typearticle
Languageen
FieldEngineering
TopicTraffic control and management
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsCrashProcess (computing)Transport engineeringComputer scienceRelevance (law)Vehicle dynamicsPoison controlSystem dynamicsEngineeringAutomotive engineering

Abstract

fetched live from OpenAlex

The framework of a systems dynamics model (SDM) is presented for simulating road crashes. For the purpose of illustration, the model is applied to two-vehicle rear-end crashes where both vehicles are assumed to be traveling in the same lane. The crash situation results from the introduction of some obstruction along the pathway, which causes drivers to adjust their speeds and separation distances. SDM extends the features of classical car-following theory in combination with crash avoidance models. Preliminary results are discussed. The SDM provides a number of useful insights into the complex process that gives rise to rear-end crashes for an assumed transportation scenario. These insights can serve to guide the development of effective safety countermeasures for reducing rear-end crashes. The potential relevance of the model for other crash situations is also discussed.

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)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.693
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.061
GPT teacher head0.329
Teacher spread0.267 · 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

Citations16
Published2001
Admission routes1
Has abstractyes

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