Vehicle Routing of Urban Snow Plowing Operations: Case Study for City of Edmonton, Canada
Bibliographic record
Abstract
Canadian municipalities spend significant capital for snow plowing to provide safe and reliable mobility for road users. Any improvement in winter snow plowing will not only result in significant capital savings for road agencies, but also improve the safety and mobility of road users. The problem of routing for snow plowing operations is generally considered a network optimization problem in the existing research. However, the formulation and solution approaches can be very different and diverse, since each area has its own unique environmental conditions and operational constraints. Assuming a district and a single depot are given, the problem is to determine a set of routes that will ensure that all road links are serviced, all the operational constraints are satisfied and the total cost is minimized. This study presents a mathematical optimization model based on the Capacitated Arc Routing Problem (CARP) to minimize the total travel distance for winter road snow plowing in the City of Edmonton. A metaheuristic algorithm is used to solve this model. The model and algorithm are applied to a road sub-network for the south part of Edmonton, Canada. The results show that the model and algorithm are capable of achieving good solutions. Sensitivity analyses also show that the final results are sensitive to the depot location and number of routes. The proposed model needs to be expanded by considering more operational constraints in the City of Edmonton.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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 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".