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Record W2164791779 · doi:10.3141/2069-08

Testing Effects of Warning Messages and Variable Speed Limits on Driver Behavior Using Driving Simulator

2008· article· en· W2164791779 on OpenAlexaff
Chris Lee, Mohamed Abdel‐Aty

Bibliographic record

VenueTransportation Research Record Journal of the Transportation Research Board · 2008
Typearticle
Languageen
FieldEngineering
TopicTraffic and Road Safety
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsDriving simulatorSpeed limitCrashComputer scienceSimulationVariable (mathematics)LogitWarning systemEngineeringTransport engineeringMathematicsMachine learningTelecommunications

Abstract

fetched live from OpenAlex

This study examines the effect of warning messages and variable speed limits (VSLs) on driver speed. Using a driving simulator, the study observed behavior of 86 participants who drove a 5-mi section of a freeway. On this freeway, three types of warning message were displayed in variable message signs (VMSs) to warn of an impending speed change. Drivers were typically required to reduce speed first and then increase speed according to VSLs. It was found that when warning messages and VSLs were displayed, participants generally drove at uniform speed and their variation in speed along the section was reduced. Statistical analysis using a binary logit model revealed that there exist correlations of driver speed changes and compliance with speed limits at successive locations of VMS. Findings of the simulator experiment suggest that warning messages and VSLs are beneficial in reducing speed variation and removing congestion. These effects of warning messages and VSLs can potentially reduce crash risk and improve efficiency on freeways.

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)0.002
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.307
Threshold uncertainty score0.785

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
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 head0.077
GPT teacher head0.335
Teacher spread0.258 · 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 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

Citations90
Published2008
Admission routes1
Has abstractyes

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