Effects of Inclement Weather Events on Road Surface Conditions and Traffic Safety: An Event-Based Empirical Analysis Framework
Bibliographic record
Abstract
Pavement friction has been widely recognized as an important winter road maintenance performance indicator for objectively assessing the level of service required to maintain safe driving conditions during inclement weather events. Despite significant efforts being put forth by road agencies, prevailing road surface conditions during snowstorms can yield negative consequences that compromise the safety of the traveling public. Acknowledging the vast road network that needs to be monitored and the uncertainty associated with the randomness of hazardous road weather conditions, this paper presents a novel event-based framework aimed at investigating the magnitude of the effect of varying pavement friction levels in urban environments on traffic safety during snowstorms. Negative binomial safety performance functions developed using hourly weather datasets and road surface conditions information found a strong statistically significant relationship between pavement friction and traffic safety. This meant that, with the accumulation of snow and ice during snowstorms, road surface conditions were found to deteriorate thereby increasing the likelihood of collision occurrence. The event-based models developed also suggested that the risks of driving during snowstorms varied dramatically depending on the varying surface states, represented by friction coefficient ranges. Collisions were expected to significantly decrease whenever the pavement friction was above 0.6; under conditions where pavement friction was below 0.35, collisions were predicted to significantly increase. Additionally, arterial roads were shown to experience a significantly higher number of collisions than collectors, further justifying why arterials should be prioritized in snow clearing policies that most cities adopt.
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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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".