Evaluating Automated Anti-Icing Technology to Reduce Traffic Collisions
Bibliographic record
Abstract
Reducing fatalities, injuries, and property damage related to traffic collisions is a priority for road safety agencies. Traffic collisions result in more than 2,200 fatalities and 173,000 injuries each year on Canadian roads. Inclement weather is a contributing factor in traffic crashes for approximately 21 percent of the injuries and 25 percent of property damage [1]. Costs related to traffic collision damage are relatively high especially if fatalities occur, and the closure of lanes and the resultant traffic delays substantially add to these costs. Efficient tools are available to road agencies to reduce traffic collisions related to inclement weather. This paper explores the relationship between road collisions and surface conditions and illustrates the successful implementation of automated anti-icing technology to reduce vehicle collisions due to weather and surface factors. Fixed Automated Spray Technology (FAST) deployments in Ontario, Utah, and Pennsylvania are specifically examined in this paper. Road surface condition sensors, automatic notification alerts, and automated anti-icing spray systems are also examined in this paper. Automated anti-icing systems minimize the amount of chemical needed for de-icing by spraying the road in advance of icing and only when required. These technologies help maintenance managers to reduce traffic collisions and fatalities and make more timely and efficient decisions. (A) For the covering abstract of this conference see ITRD record number 201310RT334E.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".