Drones Optimization for Public Transportation Safety: Enhancing Surveillance and Efficiency in Smart Cities
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
In the context of smart cities, the adoption of multi-UAV systems has become a key focus in enhancing traffic management, particularly to fortify public safety. This study addresses the challenge of optimizing traffic management through the application of swarm-based Unmanned Aerial Vehicles (UAVs). The research strategically aims to minimize the number of deployed drones for monitoring extensive road networks, fostering cost-efficiency within smart city contexts. Our investigation introduces a mathematical model, the swarm-drone set covering problem, to optimize coverage. Through a detailed computational experiment, we showcase the effectiveness of the algorithm in minimizing deployment while maintaining surveillance efficiency. Notably, our results reveal a significant correlation: as the radius of coverage for individual UAVs increases, the required number of UAVs decreases, underscoring the impact of coverage radius on resource optimization. The findings of this study contribute to the advancement of safety, security, and overall transportation network management in smart cities.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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