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Record W2612185605 · doi:10.1139/cjp-2016-0639

Improved coupled map car-following model considering partial car-to-car communication and its jam analysis

2017· article· en· W2612185605 on OpenAlexvenueno aff
Yunfeng Shi, Licai Yang

Bibliographic record

VenueCanadian Journal of Physics · 2017
Typearticle
Languageen
FieldEngineering
TopicTraffic control and management
Canadian institutionsnot available
Fundersnot available
KeywordsPhysicsNonlinear systemTraffic flow (computer networking)Automotive engineeringPenetration rateControl theory (sociology)Computer scienceControl (management)Engineering

Abstract

fetched live from OpenAlex

The characteristics and the nonlinear phenomenon of traffic flow in the case of car-to-car communication (C2CC) are studied based on an improved coupled map car-following model. The model incorporates the modified optimal velocity function and appropriate control method. The conditions necessary to maintain the system stability and suppress traffic jams are obtained. To describe the car-following dynamics under C2CC accurately, different penetration rates of C2CC vehicles, such as 10%, 30%, and 60% are considered. The simulation results suggest that the improved model can effectively suppress traffic jams. The extent to which traffic jams are suppressed is increasing as the penetration rate increases. Moreover, the car-following stability has a noticeable improvement by analysing the time–space plots.

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 score0.026
Threshold uncertainty score0.997

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 head0.016
GPT teacher head0.218
Teacher spread0.202 · 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

Citations4
Published2017
Admission routes1
Has abstractyes

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