Driver Preference and Level of Trust Related to Real-Time Road Freezing Risk Information
Bibliographic record
Abstract
Bad weather conditions can affect normal driving by substantially hindering visibility. Among all adverse weather conditions, road freezing is probably the most dangerous to drivers because slippery roads reduce surface friction and can lead to loss of vehicle control. This paper evaluates driver preference of receiving real-time road freezing risk information and explores the factors that would most influence drivers’ trust in a future road freezing information service. A survey was conducted in the metropolitan areas of South Korea during January and February 2019. The survey was completed by 231 driver’s license holders of 18 years or older, and the results were used for statistical analysis. According to the survey results, the variable message sign (VMS) is a very important system from the perspective of public benefit. Car-navigation systems are preferred for age categories of 21∼30 and over 50. In addition, ordinal regression was used to analyze the causal relationship between the level of trust regarding road freezing risk information and its controlling factors. The ordered log odds of drivers with previous accident experience due to slippery roads exhibit a higher level of trust in road freezing risk information because the coefficient is positive. Moreover, drivers with a constant commute time show a lower level of trust in road freezing risk information. These findings provide a foundation for planning the scope of future road freezing risk information service, as well as the specific service targets and type of information, especially during the winter season.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".