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Record W3114851005 · doi:10.5383/jttm.02.02.006

Effect of Distance between Ramp and Upstream Signal on Ramp Meter Operation

2020· article· en· W3114851005 on OpenAlexvenueno aff
Khaled Shaaban, Muhammad Asif Khan, Ridha Hamila

Bibliographic record

VenueInternational Journal of Traffic and Transportation Management · 2020
Typearticle
Languageen
FieldEngineering
TopicTraffic control and management
Canadian institutionsnot available
Fundersnot available
KeywordsUpstream (networking)Metering modeQueueSIGNAL (programming language)Computer scienceMetreDownstream (manufacturing)Traffic congestionControl (management)SimulationControl theory (sociology)EngineeringTransport engineeringComputer network

Abstract

fetched live from OpenAlex

Ramp metering is typically proposed as a responsive strategy that takes freeway traffic parameters as control inputs to the ramp control logic. Such a strategy can be implemented in two ways; isolated ramp control or coordinated ramp control. Coordinated ramp control typically involves the cooperation between several ramp meters connected to a freeway segment to manage traffic on the freeway and traffic all ramps. Few studies also proposed the coordination between the on-ramp and the upstream traffic signal. Such coordination can help to mitigate congestion on the freeway and to avoid queue formation at the on-ramp. In this study, the authors' previous work on ramp metering and upstream signal coordination was extended to further evaluate the performance of such schemes by considering the impact of the distance between the upstream traffic signal and freeway. Extensive simulations in SUMO were performed to evaluate the benefit of the proposed coordinated strategy and the impact of ramp distance on the effectiveness of such 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 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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.636
Threshold uncertainty score0.385

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)0.0000.000

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 head0.005
GPT teacher head0.212
Teacher spread0.207 · 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

Citations0
Published2020
Admission routes1
Has abstractyes

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