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Record W1968057764 · doi:10.1109/rsete.2011.5964831

Study on avoidance behavior model based on vehicle-bicycle collision in urban interchange

2011· article· en· W1968057764 on OpenAlexaff
Jianzhen Liu, Gang Sheng, Hui Xiong

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicTraffic Prediction and Management Techniques
Canadian institutionsMinistry of Transportation of Ontario
Fundersnot available
KeywordsOvertakingCollision avoidanceCollisionBinary logit modelComputer scienceAvoidance behaviourSimulationLogistic regressionGoodness of fitCollision avoidance systemEngineeringPsychologyMathematicsStatisticsTransport engineeringComputer securityMachine learning

Abstract

fetched live from OpenAlex

A disaggregate logit model based on collision avoidance behavior was formulated to research on the conflict of right-turning vehicles and straight bicycles in urban interchange ramp terminals. By analyzing the mechanism of the conflict of vehicle and bicycle, this paper thought that the main conflict of vehicle and bicycle could be regarded as the behavior of collision avoidance and the behavior of gap choice, and the concept of collision avoidance of vehicle and bicycle was mentioned. On the basis of the investigation, by analyzing of the factors of collision avoidance behavior and applying binary logit regression model, the disaggregate model of collision avoidance behavior was established, and then the parameters of this model was calibrated with the survey data. The results of this model test show that the value of goodness-of-fit is 4.804, and the values of accuracy for forecasting the behaviors of overtaking the bicycle and slowing down for avoidance are 91.3% and 88.9%.

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 score0.454
Threshold uncertainty score0.433

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 head0.049
GPT teacher head0.252
Teacher spread0.203 · 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

Citations2
Published2011
Admission routes1
Has abstractyes

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