Adaptive and AI-Driven Vehicle Patrol Scheduling with Integrated Emergency Response
Bibliographic record
Abstract
Vehicle Patrol Scheduling (VPS) is vital for urban security, requiring broad coverage and rapid emergency responses. This paper addresses VPS through computational optimization to enhance patrol efficiency. We propose two methods: Adaptive Hill-Climbing-Based Patrol Scheduling (AHBPS2), which uses an iterative hill-climbing strategy with integrated emergency response, and Patrol Planning with Proximal Policy Optimization $({{\mathcal{P}}^4}{\mathcal{O}})$, which models VPS as a Markov Decision Process and employs Deep Reinforcement Learning to learn optimal strategies. Extensive simulations on real-world urban maps show that ${{\mathcal{P}}^4}{\mathcal{O}}$ achieves higher visit frequencies and superior emergency handling compared to AHBPS2, underscoring the potential of these techniques for developing adaptive, efficient urban patrol systems.
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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 | 0.000 | 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 | 0.000 | 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 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".