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Record W2106143114 · doi:10.1109/iwwan.2004.1525551

Vehicle traffic microsimulator for ad hoc networks research

2006· article· en· W2106143114 on OpenAlexaff
M.M. Artimy, William Robertson, William Phillips

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicVehicular Ad Hoc Networks (VANETs)
Canadian institutionsDalhousie University
Fundersnot available
KeywordsWireless ad hoc networkComputer scienceCellular automatonMobile ad hoc networkVehicular ad hoc networkTraffic flow (computer networking)Computer networkDistributed computingMobility modelOptimized Link State Routing ProtocolAd hoc wireless distribution serviceTelecommunicationsWirelessArtificial intelligenceNetwork packet

Abstract

fetched live from OpenAlex

Host mobility patterns have an important effect on performance of ad hoc networks. For this reason, many evaluation studies involve analytical or simulation models to synthesize the movement of mobile hosts. The growing interest in investigating the use of ad hoc networks in inter-vehicle communication demands a vehicle mobility model that is capable of creating realistic movements. In fact, there exists a cellular automata (CA) model that is known for its ability to produce such movements. This paper describes a traffic microsimulator, RoadSim, which implements the basic CA model and extends it in order to generate traffic patterns suitable for use in ad hoc network research. RoadSim is capable of producing traffic that exhibits free flow characteristics, as well as start-stop waves caused by traffic jams. It can also simulate traffic in closed-loop roads or at intersections controlled by traffic signs. We intend to use this tool to evaluate ad hoc protocols in road conditions that, otherwise, cannot be reproduced without more complex and expensive traffic simulators.

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)0.001
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 score0.051
Threshold uncertainty score0.749

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.018
GPT teacher head0.264
Teacher spread0.246 · 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

Citations12
Published2006
Admission routes1
Has abstractyes

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