Snowplough service area reconfiguration using workload balancing techniques with route optimisation for large municipalities
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
Snowplowing is a complex, expensive, and mandatory winter fleet operation that benefits municipalities worldwide. In this research, three clustering approaches were used to create new snowplough route configurations for the City of Surrey, Canada, and the Smart Selective Navigator (SSN) method was used to optimise the routes. The three clustering approaches used are the current configuration-based dynamic clustering, static and dynamic clustering, and static and dynamic clustering with depot-to-cluster distance. The first clustering approach uses the existing configuration as a start point and makes minor changes, while the others generate new clusters from scratch with an objective of improving the workload distribution. SSN is a turn-based route optimisation algorithm that was improved by adding advanced turn-tracking methods capable of generating feasible routes in complex geographic information system (GIS) road network data. The simulation results show improvements when high-priority roads are clustered using the minor modification approach, and lower-priority roads are clustered from scratch. Overall, the clustering approaches can save 51 min of simulated travel time while significantly improving the workload balance.
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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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