Optimization of Winter Road Maintenance Under Traffic and Weather Information
Bibliographic record
Abstract
The total estimated direct annual cost of winter maintenance amounts to $1 billion for Canada and $2 billion for the United States. Municipalities seek technology-based solutions such as information systems to optimize operations and reduce costs. As an example, Advanced Road Weather Information Systems provide real time weather forecasts which, coupled with traffic information systems, can be influential in reducing maintenance costs. The authors present, in this paper, a model for managing winter road maintenance operations under weather and traffic information. The model captures the interactions between plow (spreader) trucks, height of snow on ground, and corridor traffic. An extension of the original model is formulated which considers multi-segment corridors with on-ramps and off-ramps. Results show that traffic is barely affected in cases where the storm peak occurs later than the traffic peak. The model also indicates the benefits of sending multiple plow (or spreader) trucks. In general, dispatching multiple maintenance trucks is justified in cases where the storm peak is relatively close to the traffic peak. Sensitivity analysis on the multi- segment corridor model shows that having a higher number of maintenance trucks increases dispatching flexibility which can consequently reduce delays extensively.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".