Making Roads Safer: Optimizing De-icing Using Snowmelt Rates and Slope Data
Bibliographic record
Abstract
More than $1.5 billion is spent annually on winter road maintenance programs in the United States.With an increase in severe winter storms, many municipalities are encountering difficulty in clearing their roads of ice and snow within their budgetary limits.Currently, roads are classified based on traffic counts, with busier (arterial) roads being treated before less busy (secondary) roads.This approach is problematic because most people live on secondary roads and must travel over these untreated roads before they can reach treated roads.In addition, inefficient distribution of de-icing material can have knock-on environmental impacts with increased material run-off into surrounding areas.To address these concerns, we proposed a road vulnerability index based on rate of snowmelt and road slope.We calculated the snowmelt rates in 1-m by 1-m squares of all roads in Knox County, Tennessee, using an empirical equation developed by the U.S. Army Corps of Engineers.This equation takes in freely available weather data and incident solar radiation, which we calculated based on LiDAR (Light Detection And Ranging) data.Using the model results, we propose a more efficient winter road maintenance strategy: apply de-icers to the steepest roads with the slowest snowmelt, rather than to the busiest roads.Under this approach, we argue that given existing budgetary constraints, more vulnerable roads can be treated with de-icing material and provide for easier mobility for motorists traveling on secondary roads.Our approach provides a more cost-effective, environmentally-conscious, and mobility enhancing application strategy.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".