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Record W3138434708 · doi:10.37394/23202.2020.19.4

Real Time Intelligent Traffic Light System

2020· article· en· W3138434708 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueWSEAS TRANSACTIONS ON SYSTEMS · 2020
Typearticle
Languageen
FieldEngineering
TopicTraffic control and management
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsQueueMATLABComputer scienceTraffic congestionReal-time computingAutomotive engineeringFloating car dataTraffic signalTraffic optimizationIntelligent transportation systemControl (management)SimulationTransport engineeringEngineeringComputer networkArtificial intelligence

Abstract

fetched live from OpenAlex

with the increasing vehicle ratio on our roads, a lot of factors such as pollution, time constraints and environmental factors need to be addressed. One main issue to be addressed is traffic congestion during the peak hours. This issue affects drivers in numerous ways including loss of productive working hours by lining up in traffic queue. It also leads to loss of natural resource such as fossil fuel used by the vehicle engine while the car is running but lining up in traffic. In this paper, we propose an intelligent sensor-based traffic light control system. To analyze and compare performance, the system was implemented in MATLAB/SIMULINK and simulation results demonstrate the feasibility and high accuracy of the proposed intelligent traffic light system.

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.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.939
Threshold uncertainty score0.999

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)0.0000.002

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 head0.009
GPT teacher head0.181
Teacher spread0.172 · 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