A Correlation Between Road Surface Conditions and Road Traffic Incidents with Personal Injuries in Cold-Climate Areas
Bibliographic record
Abstract
In cold-climate areas, variations in weather conditions during the winter can cause unsafe road transportation.Slippery road can be a result of different winter weather conditions (e.g., snow, sleet, and freezing rain), which can lead to severe road accidents.Therefore, finding a relationship between various weather conditions and road accidents plays a crucial role in road traffic safety and maintenance approaches.Hence, this study aims to identify the impact of different weather conditions (e.g., snow, sleet, and freezing rain) on the road surface conditions (RSCs) and to use this information to find a correlation between road conditions and road accidents with personal injuries at Testsite E18 in Sweden.In this study, data points are recorded between the years 2019 and 2023.Three different sensors were used to measure road surface conditions: a sensor mounted in the wheel track, a sensor mounted in the middle of the roadway, and an optical sensor.In addition, road weather station data were used to extract the precipitation to find possible situations with sleet or freezing rain.Finally, multi-sensor data and road accidents with personal injuries were analyzed by use of a qualitative approach to find a correlation between different road weather conditions and road accidents.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".