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Record W2133227382 · doi:10.3141/2423-07

Empirical Evaluation of Drivers’ Route Choice Behavioral Responses to Social Navigation

2014· article· en· W2133227382 on OpenAlexaff
Shadi Djavadian, Raymond Hoogendoorn, Bart van Arem, Joseph Y.J. Chow

Bibliographic record

VenueTransportation Research Record Journal of the Transportation Research Board · 2014
Typearticle
Languageen
FieldEngineering
TopicEvacuation and Crowd Dynamics
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsIncentiveAdvice (programming)AsideTravel behaviorPsychologyApplied psychologyTransport engineeringComputer scienceComputer securityEngineering

Abstract

fetched live from OpenAlex

Even though route choice behavior and drivers’ acceptance of advanced traveler information systems have been studied in the past, little or no attention has been given to the route choice behavior and acceptance response to social navigation systems. What separates social navigation systems from traditional traffic navigation is that the route advice aims to minimize the individual travel time and the marginal total travel time in the network. In this study, drivers’ behavioral responses to social navigation route guidance were empirically evaluated under different information and incentive strategies. A traffic navigation application based on social navigation was developed and used in a pilot multiuser laboratory experiment. Participants were asked to make route choices in a virtual travel environment under various information and incentive strategies. Drivers were more willing to comply with the social advice when they were well informed and well rewarded. The results also show that female and novice drivers are more willing to comply with the social advice than are male drivers and experienced drivers. Aside from the level of altruism, a driver's indifference to switching routes also affects a driver's compliance with social advice.

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)0.009
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 score0.178
Threshold uncertainty score0.751

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.183
GPT teacher head0.466
Teacher spread0.283 · 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

Citations30
Published2014
Admission routes1
Has abstractyes

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