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Record W1965061176 · doi:10.1109/pimrc.2011.6140066

CVI: Connected Vehicle Infrastructure for ITS

2011· article· en· W1965061176 on OpenAlexaff
Agop Koulakezian, Alberto Leon‐Garcia

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicMobile Ad Hoc Networks
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsCluster analysisComputer scienceOverhead (engineering)Resilience (materials science)Intelligent transportation systemComputer networkWireless sensor networkDistributed computingAggregate (composite)Real-time computingEngineeringArtificial intelligenceTransport engineering

Abstract

fetched live from OpenAlex

Intelligent Transportation Systems (ITS) aggregate, analyze and display geographic- and temporal-specific sensor information to reduce congestion while promoting safety. Until now, the coverage and potential of ITS have been restricted by the excessive cost of deploying the required road sensor and communications infrastructure. Our solution to this problem is focused on a novel integrated ITS Network Architecture where vehicles are the main infrastructure in the network. We propose The Connected Vehicle Infrastructure (CVI) for Intelligent Transportation Systems (ITS), where vehicles adapt to mobility changes to form stable vehicular clusters using a Network Criticality-based algorithm we have developed. Further, they build on their clusters to form more stable Mobile Networks, as part of the ITS network. Simulation results using NS-2 show that CVI clustering provides more stable clusters, lower handoffs, higher resilience to errors and better connectivity than popular density-based vehicle clustering methods. In addition, the overhead analysis of CVI shows that it achieves reasonable overhead compared to common clustering algorithms.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.868
Threshold uncertainty score0.367

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 science0.0010.000
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 head0.024
GPT teacher head0.225
Teacher spread0.201 · 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 designTheoretical or conceptual
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

Citations11
Published2011
Admission routes1
Has abstractyes

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