Physical and economic impacts of studded tyre use on pavement structures in cold climates
Bibliographic record
Abstract
ABSTRACT In cold regions like Alaska of USA, Canada and the northern parts of Europe, using studded tyres is common among the public when driving in icy and snowy conditions. However, studded tyres cause extensive wear to asphalt pavement, reducing pavement life. This study addresses the physical and economic impacts of winter studded tyres on the roadway system to better inform decision makers as they develop alternative solutions and future polices. The approach is applied in a case study from a sample of Alaska statewide road segments. Surveys were employed to examine the extent of the use of studded tyres and cost-effective alternatives. A pavement life-cycle cost review was established considering several variables to discover a realistic cost of roadway resurfacing and rehabilitation. Wear rates due to studded tyres and rut rates due to wheel loads were found for different highway classes. The results indicate higher average wear rates due to studded passenger vehicles on freeways than average rut rates due to heavy wheel loads. The results also indicate lower average wear rates on arterial and collector roads. The estimates show that studded tyre use reduced asphalt surface life by about 7 years on the selected freeway sample in the case study, which is about 47% loss in pavement life based on the initial design life of 15 years. Other road classes experienced lower reductions in service life. Finally, cost analysis was provided to reflect the impact of studded tyres on the state's budget. Countermeasures were suggested, which in turn may help other cold regions develop strategies on the use of new winter tyre technology.
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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 | 0.001 | 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".