Unmanned aerial vehicle traffic network design with risk mitigation
Bibliographic record
Abstract
As unmanned aerial vehicle (UAV) technology continues to advance, more retailers are considering UAVs as a viable alternative to ground-based transportation for package delivery. While the literature has investigated UAV-based delivery, it often assumes UAV movement within a point-to-point Euclidean distance from origin to destination, due to the size and mobility of UAVs. However, this straight path movement raises significant air traffic management concerns, making it impractical as drone deployment scales up. In this research, we investigate the optimal UAV service network when UAV travel is restricted to flying over street networks in urban areas, as proposed by our industry partner AirMatrix. The goal is to determine the optimal subnetwork — consisting of street segments over which UAV travel is permitted — that balances demand fulfillment and risk mitigation. We propose a bi-objective mixed integer programming model for UAV network design which minimizes risk while maximizing satisfied demand subject to budgetary constraints. To model drone travel risk, we propose six risk measures motivated by practical regulatory considerations and traffic risk research, aimed at reducing total risk, worst-case risk, or risk variance within the network. We further propose a two-stage stochastic programming variant to design a UAV network that is robust to uncertain demand. We show that for one of the proposed risk measures, the stochastic problem and the mean-value deterministic problem result in equivalent optimal UAV network decisions. To the best of our knowledge, the proposed models are the first in the literature to explicitly incorporate risk considerations into UAV traffic network design. Extensive computational experiments are conducted in a case study of suburban Miami, using real street network data, to evaluate the performance of the various risk objectives with respect to the overall network design, path-based and arc-based performance metrics. The results indicate that the total risk and the total risk deviation objectives achieve the best overall performance and effectively minimize risk across various metrics at the least cost.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".