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Record W2520870002 · doi:10.1002/atr.1405

The speed control effect of highway tunnel sidewall markings based on color and temporal frequency

2016· article· en· W2520870002 on OpenAlexvenueno aff
Hongliang Wan, Zhigang Du, Qixiang Yan

Bibliographic record

VenueJournal of Advanced Transportation · 2016
Typearticle
Languageen
FieldPsychology
TopicSafety Warnings and Signage
Canadian institutionsnot available
FundersNational Natural Science Foundation of China
KeywordsLuminanceIllusionDriving simulatorSimulationPerceptionStimulus (psychology)EngineeringComputer scienceComputer visionPsychology

Abstract

fetched live from OpenAlex

Summary The low‐luminance monotonous environment in the middle section of highway tunnels offers few reference points and is prone to cause severe visual illusion. Thus, drivers tend to underestimate their driving speed, which can induce speeding behaviors that result in rear‐end collisions. Therefore, discovering low‐cost methods of traffic engineering that reduce this visual illusion and ensure a steady driving speed is an important challenge for current highway tunnel operations. This study analyzes the effects of sidewall markings in typical highway tunnels, specifically observing how their colors and temporal frequencies affect the driver's speed perception in a low‐luminance condition. A three‐dimensional model of the middle section of highway tunnels was built in a driving simulator. Psychophysical tests of speed perception were carried out by the method of limits. The precision of the simulation model was then checked by comparing the results to field test data. The simulation tests studied the stimulus of subjectively equal speed and reaction time in relation to sidewall markings in different colors (red–white combined, yellow–white combined, and blue–white combined). Furthermore, based on the optimal color, the effects of sidewall marking with different temporal frequencies (0.4, 0.8, 1.2, 2, 4, 8, 12, 16, and 32 Hz) on the speed perception of drivers were also analyzed. The test results reveal that the color and temporal frequency of sidewall marking have a significant impact on the driver's stimulus of subjectively equal speed and reaction time. The subjects have the highest speed overestimation and an easy speed judgment with the red–white combined sidewall marking. Within the temporal frequency range of 4.45–7.01 Hz, the subjects have a certain degree of speed overestimation (less than 20%), and the speed perception is sensitive to the temporal frequency changes. Copyright © 2016 John Wiley & Sons, Ltd.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.006
GPT teacher head0.253
Teacher spread0.247 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations17
Published2016
Admission routes1
Has abstractyes

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