Scalable Grid-Based Design of Highway-in-the-Sky Networks with OD-Agnostic Optimization
Bibliographic record
Abstract
This paper presents a novel and computationally efficient model for designing Highway-in-the-Sky (HITS) networks to support scalable Unmanned Aerial System (UAS) and Advanced Air Mobility (AAM) operations. The proposed approach formulates the HITS network design as a grid-based optimization problem that minimizes the cumulative risk associated with the grid cells occupied by the network. Without compromising model integrity, the approach is illustrated through an equivalent objective that minimizes the number of activated grid cells. The solution maintains origin–destination (OD) connectivity and enforces flow conservation. In contrast to previous routing-based formulations, this method reduces algorithmic complexity from $\mathcal{O}\left( {{G^2} \times P} \right)$ to $\mathcal{O}(G)$, where G is the number of grid cells and P is the number of OD pairs. This OD-agnostic formulation enables large-scale deployment without performance degradation. A comparative analysis between the routing-based and grid-based approaches demonstrates significant improvements in scalability, and a case study validates the model’s key feature—that the solution time is independent of the number of OD pairs. The paper also outlines potential extensions involving 3D layered routing, risk-aware network design, and real-time simulation-based validation.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 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) | 0.003 | 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".