Smarter Patrols, Safer Streets: a Dynamic Framework for Crime-Aware Routing and Replanning
Bibliographic record
Abstract
Effective patrol planning is central to public safety and resource optimization. We present a datadriven framework that fuses historical crime-trend analytics with real-time patrol routing cast as a Dynamic Vehicle Routing Problem (DVRP). Using Canadian provincial crime statistics (2010--2022), we extract temporal trends and hotspot offences and translate these into operational priorities for patrol allocation. A Pythonbased simulator evaluates heuristic strategies (nearestneighbor and risk-weighted patrolling) with incidenttriggered re-optimization. Results show that attention to high-risk areas can be increased without sacrificing responsiveness to stochastic calls for service, while also improving workload balance across units. The approach is modular and deployment-ready: it extends naturally to metaheuristics and reinforcement learning for policy optimization and integrates with GIS/CAD/AVL infrastructure for city-scale operations.
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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 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".