Deploying Multiple Vehicles for Snow Plowing Using Smart Selective Navigator and its Effect
Bibliographic record
Abstract
Winter operations, such as snowplowing and salt spreading, have far-reaching implications for businesses, road safety, and mobility. This paper investigates the effect of deploying multiple vehicles for the same set of streets in winter operations using the Smart Selective Navigator (SSN) method. The study focuses on selected regions of the City of Oshawa. A mathematical model and problem formulation are presented, outlining the representation of the road network and variables used in the SSN method. The SSN method utilizes a non-backtracking approach and assigns target nodes to vehicles based on scoring criteria. Simulation results show that deploying multiple vehicles reduces the operation time almost linearly, indicating improved efficiency and productivity. However, an increase in the fleet size also leads to a slightly higher total distance covered, impacting fuel consumption and maintenance costs. The findings highlight the potential benefits of deploying multiple vehicles in winter operations, but careful consideration of trade-offs is necessary. This study provides insights to inform businesses and policymakers on resource allocation and optimizing winter operations for enhanced efficiency and effectiveness. Further analysis is needed to fully understand the trade-offs and optimize winter operations effectively.
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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".