Effects of Simulated Day and Night Driving on the Speed Differential in Tangent–Curve Transition: A Pilot Study Using Driving Simulator
Bibliographic record
Abstract
OBJECTIVE: The pilot study described in this article aimed to analyze the driver speed profile for evaluation of road design consistency during simulated day and nighttime driving. The research, carried out using a driving simulator, was developed with the overall objectives of evaluating the speed differential during simulated nighttime driving for the identification of critical road situations not detected by design consistency evaluation during simulated daytime driving. METHODS: An existing 2-lane rural road, where high accident rates were recorded during nighttime, was implemented in the driving simulator of the Inter-University Research Centre of Road Safety (CRISS) and the drivers' speed profiles were recorded in both simulated day and nighttime driving conditions over the 39 tangent-curve configurations that composed the road alignment. RESULTS: The analysis of the speed differential based on the 85MSR (Maximum Speed Reduction) indicator during simulated daytime driving was not able to identify critical road situations that the same analysis revealed during the simulated nighttime driving. Such results occurred for most of the tangent-curve configurations. CONCLUSIONS: The study demonstrated that limiting the speed analysis only to daytime driving conditions cannot exclude the possibility that during nighttime driving some road configurations could become unsafe. The findings of the study highlight the need to carry out design consistency evaluations for nighttime driving conditions.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".