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Record W4236875757 · doi:10.32920/ryerson.14658147

Traffic signal coordination for Wellington Street West, Toronto, Ontario

2021· preprint· en· W4236875757 on OpenAlexaffabout
Sharareh Shadbakhsh

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicTraffic Prediction and Management Techniques
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsPlatoonTransport engineeringDestinationsTraffic congestionTraffic signalSIGNAL (programming language)Traffic intensityTraffic volumeTravel timeService (business)Computer sciencePublic transportFuel efficiencyTelecommunicationsBusinessGeographyReal-time computingEngineeringTourismAutomotive engineeringControl (management)ArchaeologyMarketing

Abstract

fetched live from OpenAlex

The increasing volume of traffic in cities has a significant effect on road traffic congestion and the travel time it takes for road users to reach their destinations. Coordinating traffic signals, which is a system of light that cascade in sequence where a platoon of vehicles can travel through a continuous series of green light without stopping, can improve the driver's experience significantly. This report covers the development of a coordinated traffic signal system along Wellington Street West from Church Street to Blue Jays Way Street as part of a City of Toronto signal coordination project. The objective of this study is to improve coordination through modification of signal timing plans while maintaining reasonably minimal impacts to the side street levels of service and delays. The overall goal is to reduced travel times, delays, number of stops and fuel consumption, resulting in public benefit.

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)0.000
metaresearch head score (Gemma)0.001
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: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.160
Threshold uncertainty score0.535

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.000
Scholarly communication0.0020.000
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.1600.024

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 head0.014
GPT teacher head0.223
Teacher spread0.210 · 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
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

Citations0
Published2021
Admission routes2
Has abstractyes

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