Unmanned Aerial Vehicle Traffic Network Design with Risk Mitigation
Bibliographic record
Abstract
As unmanned aerial vehicle (UAV) technology becomes more robust and widespread, \nmore and more retail companies are seeing UAVs as a suitable alternative to ground-based transportation to deliver their packages. As a result, there has been an abundance \nof OR research focused on UAV utilization for last-mile delivery. Due to the size and \nmobility of UAVs, most of this research considers UAV movement within a shortest path or \nEuclidean shortest path context. While this may be plausible if drone usage remains sparse, \nthis framework will not be possible as drone utilization ramps up to the levels required \nto satisfy the levels of package demand expected in the coming decades. Furthermore, \nnone of this prior research (to our knowledge) suggests using risk inherent with UAV \ntravel to influence their proposals from a logistical and/or modelling perspective. As a \nsolution to this problem, our industry partner AirMatrix proposes that UAV travel be \nrestricted to transportation networks situated above the streets of population centres. We \npropose a bi-objective network selection model for drone delivery which minimizes risk \nwhile maximizing the amount of satisfied demand subject to budgetary constraints. We \ndiscuss the factors that affect UAV risk and what metrics can be used to effectively reduce \nthose factors from a modelling perspective. We propose a two-stage stochastic variant of the \nmodel and additional problem requirements to reflect practical operational requirements \nand design goals. Using sample average approximation, we show that a deterministic \nsolution is effectively as good as an associated stochastic solution. We conduct testing on \na region of suburban Miami to evaluate how different risk objectives perform with respect \nto network, path, arc, and performance metrics.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".