Investigation of the Influence of the Condition of Asphalt Pavement Surface on Road Safety of Rural Ontario Highways
Bibliographic record
Abstract
Road collisions are complex events that are influenced by a combination of factors, including driver behaviour, environmental condition (e.g., icy and wet roads), road geometry, roadside elements, vehicle speed, tire deficiencies, traffic, and pavement condition.While the influence of some of these factors has been studied extensively for decades, the influence of pavement condition on road safety is relatively underresearched.This research investigated the influence of pavement surface condition on road safety by developing statistical models that correlate pavement surface condition and collisions.This research also examined the possibility to integrate skid resistance into pavement management by investigating the correlations between skid resistance, pavement distress, and operational conditions of the roads.This study was limited to rural arterial and freeways of the Ontario asphalt pavement road network.Data of pavement condition, operational condition, and collision was obtained from the Ontario Ministry of Transportation for 6879 kilometers across 37 provincial rural highways for the period of 2012 to 2014.Pavement condition data was collected at network level with an automatic road analyzer road and included information about roughness, rutting, cracking, and macrotexture.Skid resistance data was collected with a locked wheel tester.The collected data was combined into a spatial data model, also known as a vector-based geographic information system.The results of the investigation using regression analysis showed that pavement friction is affected by traffic, pavement age, and pavement distress.Skid resistance decreased with the increase of traffic and increased with the increase of pavement distress.Macrotexture increased with the increase of traffic and pavement distress.
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 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 | 0.001 | 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) | 0.001 | 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".