Optimization of snow plowing cost and time in an urban environment: A case study for the City of Edmonton
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Each winter, Canadian municipalities deploy significant capital for snow plowing. Any improvements to snowplow operations not only results in significant capital savings for municipalities and road agencies, but also improves roadway safety and user mobility. In the existing research, routing snowplow operations is generally considered a network optimization problem; however, the formulations and solutions can be very diverse, as each urban area has unique environmental conditions and operational constraints. For a specific district and depot, the problem is determining a set of routes that ensure that all road links are serviced, all operational constraints are satisfied, and total operational costs are minimized. This study used a mathematical optimization model based on the capacitated arc routing problem (CARP) to minimize the total travel distance for snowplow operations in the City of Edmonton. Depot location and route number are critical input parameters to the operation cost control. Sensitivity analyses were conducted to not only derive snowplow routing strategies using the CARP methodology, but also draw useful conclusions for winter road maintenance planners.
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.
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 it