Scheduling resources in a mobile photo enforcement program
Bibliographic record
Abstract
This paper presents a model for scheduling resources in a mobile photo enforcement (MPE) program. An MPE program deploys operators driving vehicles equipped with radar and photo equipment to roadway locations where speeding and collision problems are significant. We developed a binary integer linear programming model to allocate MPE shifts over the course of a month to selected enforcement locations. These locations are pre-determined from a set of city neighborhoods chosen for enforcement through a prior multi-objective programming step. A major feature of the scheduling model is to observe the time halo effects of enforcement; our optimization model minimizes visits to an enforcement location over consecutive shifts. The model was applied to data obtained from the currently operating MPE program in Edmonton, Canada. Based on the resource allocation in neighborhoods determined in the previous stage, this scheduling model produces a scheduling plan that allocates resources to individual enforcement sites within each city neighborhood during a month. The purpose of this scheduling model, in combination with the first stage neighborhood allocation model, is to provide enforcement agencies with a tool to systematically and transparently assign limited resources in an efficient manner, providing greater efficacy in achieving program-level objectives such as reducing speeding, reducing collisions, and providing enforcement presence in areas with many vulnerable pedestrians (i.e. school zones).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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 source (direct Gemma or distilled Codex), 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".