Optimizing snowplow routes using all-new perspectives: road users and winter road maintenance operators
Bibliographic record
Abstract
Snowplowing is an indispensable part of winter road maintenance because it contributes to improving drivers’ mobility and safety. Most prior studies, however, limit focus to operational aspects without considering interaction with road users. This paper aims to minimize travel time in the traffic system while still maintaining the efficiency of plowing activities. The k-truck plowing with the precedence model was formulated to link road users with snowplow activities. The tabu search algorithm then followed to optimize the order of precedence of snowplow routes. Furthermore, the discrete-event simulation was used to quantify the effectiveness of the proposed method based on different scenarios and fleet sizes, and to illustrate the potential benefit of integrating both the operators’ and users’ perspectives. The methodological framework developed herein can be used to design a routing strategy that improves the performance of the snowplow trucks by reducing both plowing completion and road users’ total travel time.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".