MétaCan
Menu
Back to cohort
Record W2926790693 · doi:10.1155/2019/2196431

Modelling the Effect of Mobile Phone Use on Driving Behaviour Considering Different Use Modes

2019· article· en· W2926790693 on OpenAlexvenueno aff
Ioanna Spyropoulou, Maria Linardou

Bibliographic record

VenueJournal of Advanced Transportation · 2019
Typearticle
Languageen
FieldPsychology
TopicHuman-Automation Interaction and Safety
Canadian institutionsnot available
Fundersnot available
KeywordsDistractionMobile phoneMobile devicePhoneLegislationSAFERDistracted drivingMode (computer interface)Mobile phone trackingComputer scienceGSM servicesHuman–computer interactionMobile technologyComputer securityTelecommunicationsPsychology

Abstract

fetched live from OpenAlex

Mobile phone use while driving is a major cause of driver distraction, affecting driving performance and increasing accident risk. Governments have responded to this with the implementation of legislation prohibiting the use of mobile phones, under specific conditions, mainly adopting the hands-free use. Still, mobile phone is a cause of several types of distraction rather than just manual. This study explores the effect of mobile phone use while driving via a simulator experiment. Participants drive under various types of mobile phone use mode- namely, handheld, hands-free (wired earphone), and speaker to capture this effect. Results highlight the effect of mobile phone use, regardless of the use mode, on driving behaviour through specific indicators: maximum driving speed, reaction time, and lateral position. In particular, considering the aforementioned parameters the handheld mode demonstrates safer driving behaviour compared to the speaker mode. The results of this study stress the need for a reconsideration of the present legislation.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.914
Threshold uncertainty score0.406

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 communication0.0000.001
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 head0.023
GPT teacher head0.321
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.

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

Citations26
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Advanced TransportationSame topicHuman-Automation Interaction and SafetyFrench-language works237,207