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Record W601179032 · doi:10.32920/25413064.v1

Human Factors Considerations in the Design of Rumble Strips

2024· preprint· en· W601179032 on OpenAlexaff
Frank Russo, Jeffery A. Jones

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicManufacturing Process and Optimization
Canadian institutionsWilfrid Laurier UniversityToronto Metropolitan University
Fundersnot available
KeywordsRumbleSTRIPSComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

<p>Although a number of technical reports have considered different aspects of construction on crash statistics, human factors considerations are less prevalent, and no study has examined the perceptual consequence of altering the frequency of the repetition pattern. In experiment 1, the effect of frequency on perceived urgency in an auditory-only simulation of rumble strips was considered using a magnitude-estimation task. Results indicated that the ideal frequency range (yielding the highest urgency) was between 12.5 to 25 Hertz (Hz), which is lower than the range produced by typical rumble strip spacing. A psycho-acoustic explanation of this result is that the frequency range between 12.5 to 25 Hz is within the range in which sequential noise bursts can be resolved but below the range in which noise bursts fuse and give rise to pitch perception. In experiment 2, participants estimated pitch strength and matched the pitch of simulated rumble sound to a pure tone. As expected, pitch strength estimates and the accuracy of pitch matches were consistently low between 17 to 50 Hz but increased linearly with frequency beyond 50 Hz. On the basis of this auditory-only simulation, it appears that rumble strip spacing leading to audible but infra-pitch sound is ideal.</p>

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.542
Threshold uncertainty score0.341

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 head0.051
GPT teacher head0.258
Teacher spread0.208 · 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
Published2024
Admission routes1
Has abstractyes

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