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Record W2570620110

Operating Performance of Automated Pedestrian Detectors at Signalized Intersections

2010· dissertation· en· W2570620110 on OpenAlexaboutno aff
Jonathan Foord

Bibliographic record

VenueMspace (University of Manitoba) · 2010
Typedissertation
Languageen
FieldEngineering
TopicTraffic Prediction and Management Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsPedestrianTransport engineeringDetectorComputer sciencePedestrian detectionEngineeringTelecommunications
DOInot available

Abstract

fetched live from OpenAlex

The research analyzes the operating performance of three commercially available curbside automated pedestrian detectors (APDs) (infrared and stereovision, passive infrared, and a microwave detector) for the actuation of pedestrian walk phases as a function of winter weather and temperature variations at signalized intersections in terms of detector selectivity and sensitivity. Two sites were selected for field analysis in Winnipeg, Manitoba Canada. Based on a sample of 8,225 detections at the two sites, the research found that overall sensitivity rates of the APDs ranged from 62 to 98 percent while selectivity rates were generally below 50 percent. Regardless of site, the infrared/video APD had the second highest sensitivity and highest selectivity rates of all APDs analyzed. The infrared APD had the highest sensitivity and lowest selectivity rates, and the microwave APD had the lowest sensitivity and second highest selectivity.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.654
Threshold uncertainty score0.936

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 head0.007
GPT teacher head0.190
Teacher spread0.183 · 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 designBench or experimental
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

Citations1
Published2010
Admission routes1
Has abstractyes

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