Smart selective navigator (SSN): enhancing urban winter road maintenance through optimized arc routing with hard turn restrictions
Why this work is in the frame
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Bibliographic record
Abstract
Abstract This paper introduces a novel heuristic method, the smart selective navigator (SSN), for addressing arc routing problems (ARPs) with a focus on integrating hard turn restrictions in urban winter operations. Addressing a significant gap in existing ARP methodologies, SSN seamlessly incorporates common side constraints, such as vehicle characteristics and road priorities, while strictly adhering to turn restrictions. Mathematically, the approach involves representing urban road networks as directed multigraphs. SSN's effectiveness was demonstrated through a case study on winter road maintenance in the City of Oshawa, which showed improved operation times. This study not only fills a crucial research gap in ARP but also offers a versatile solution applicable to various urban routing challenges, with potential applications extending beyond winter operations. Future research directions include exploring dynamic weighting models further and replacing classical optimization methods with machine learning for real‐time route generation.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it