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Record W4381571936 · doi:10.1080/03081060.2023.2214144

Lane-based analysis of the saturation flow rate considering traffic composition

2023· article· en· W4381571936 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

VenueTransportation Planning and Technology · 2023
Typearticle
Languageen
FieldEngineering
TopicTraffic control and management
Canadian institutionsUniversity of Manitoba
FundersQatar National Library
KeywordsIntersection (aeronautics)Transport engineeringOutcome (game theory)Saturation (graph theory)Metric (unit)Traffic flow (computer networking)Computer scienceEconometricsSimulationEngineeringMathematicsEconomicsComputer securityOperations managementMicroeconomics

Abstract

fetched live from OpenAlex

Saturation flow rate (SFR) is an essential metric for estimating the capacities of signalized intersections. Many factors, including traffic composition, configuration and geometry of the intersection, and driver behavior, which is typically characterized by social and cultural norms, influence SFR. Most of the previous studies estimated the SFR and adjustment factor to be applied independently without considering the interaction impact between influencing factors. This study aims to empirically examine the influence of the number of lanes, the heavy vehicle proportions, and their interaction effect on the SFR of through movements. A new model was developed to magnify the HV Impact on SFR value considering the number of lanes at the upstream approach. The outcome of this study helps to improve the multiplicative model’s structure for SFRs adjustment factors. Adopting the outcome of this study by the responsible transport authority would optimize the road infrastructure provision.

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 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 score0.279
Threshold uncertainty score0.210

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.008
GPT teacher head0.200
Teacher spread0.193 · 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