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Record W576447651

Study on Calibration and Validation of Fundamental Diagram for Urban Arterials

2012· article· en· W576447651 on OpenAlexaboutno aff
Md. Ahsanul Karim, Tony Z. Qiu

Bibliographic record

VenueTransportation Research Board 91st Annual MeetingTransportation Research Board · 2012
Typearticle
Languageen
FieldEngineering
TopicTraffic Prediction and Management Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsDiagramFlow (mathematics)Traffic flow (computer networking)CalibrationMaximum flow problemData collectionStatisticsSimulationStatistical physicsMechanicsEnvironmental scienceMeteorologyComputer scienceMathematicsPhysicsMathematical optimization
DOInot available

Abstract

fetched live from OpenAlex

Fundamental diagram (FD) describes the relationship between the macroscopic traffic parameters - traffic speed, flow and density. This study aimed at investigating the existence and properties of fundamental diagrams on urban arterials. The research first investigated the efficacy of estimating speed-flow relationship on 97 Street, one of the major arterials in the City of Edmonton, using the Webster’s delay formula. Then the existence of arterial fundamental diagram was investigated by analyzing the field data collected from the groundhog sensors installed on the study corridor. Finally, this study also explored several traffic flow characteristics by analyzing the speed-flow-density diagrams obtained from the micro-simulation model of 97 Street. The results from the theoretical analysis showed that the Webster’s delay formula could be successfully used to estimate the speed-flow relationship on any under-saturated signalized segment of urban arterials. The existence of an arterial fundamental diagram linking flow and density was also observed from the field data. The outcome of the micro-simulation model supported these results, and thus confirmed the existence of FD on any arterial segment. However, both the maximum flow and the maximum density from the simulations results were observed to be slightly larger than those obtained from the groundhog data. When the data from individual segment were aggregated to investigate the speed-flow-density relationships for the whole study corridor, somewhat chaotic scatter-plots were found. The presence of traffic signals might be the reason for this scatter.

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)0.005
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.184
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.075
GPT teacher head0.373
Teacher spread0.298 · 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.

Study designObservational
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

Citations2
Published2012
Admission routes1
Has abstractyes

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