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Record W606386386

Integrated Regional Signal System Benefits of Regional Traffic Signal Timing

2009· article· en· W606386386 on OpenAlexaboutno aff
J K Lam, K Kitasaka

Bibliographic record

Venue2009 ANNUAL CONFERENCE AND EXHIBITION OF THE TRANSPORTATION ASSOCIATION OF CANADA - TRANSPORTATION IN A CLIMATE OF CHANGE · 2009
Typearticle
Languageen
FieldEngineering
TopicTraffic Prediction and Management Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsSIGNAL (programming language)Signal timingComputer sciencePlan (archaeology)Transport engineeringFocus (optics)Traffic signalTelecommunicationsOperations researchReal-time computingEngineeringGeography
DOInot available

Abstract

fetched live from OpenAlex

Traffic signal management systems have been proven to demonstrate significant benefits from the coordination of traffic signal operations. However, with the continued growth of the various urban areas in the Metro Vancouver area and the installation of an increasing number of signalized intersections, there is a growing need to coordinate the traffic signal operations between adjacent municipalities and agencies. The goal of the Integrated Regional Signal System (IRSS) program is to make better use of available information and communications technologies to provide a 'system of systems' that facilitates coordinated operation between individual municipalities and jurisdictional agencies, while also allowing the individual agencies to maintain their autonomy with respect to signal control equipment selection and signal timing plan implementation. This paper will present an overview of the IRSS operations and summarize the results of the 'before and after' studies with a focus on the benefits including the estimated GHG reductions attributed to improved signal coordination.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.989
Threshold uncertainty score0.037

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0110.001

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 head0.025
GPT teacher head0.212
Teacher spread0.187 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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

Citations0
Published2009
Admission routes1
Has abstractyes

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Same venue2009 ANNUAL CONFERENCE AND EXHIBITION OF THE TRANSPORTATION ASSOCIATION OF CANADA - TRANSPORTATION IN A CLIMATE OF CHANGESame topicTraffic Prediction and Management TechniquesFrench-language works237,207