Airspace network design for urban UAV traffic management with congestion
Bibliographic record
Abstract
To support the safe and widespread use of unmanned aerial vehicles (UAVs) in urban environments, industry stakeholders and regulatory authorities are partnering to develop urban airspace traffic management systems (UTMs). UTM system providers face strategic decisions in how to design and manage airspace available to UAV flights. We consider a provider that plans to open an urban airspace in which UAV flights are routed above existing roads in 3D corridors corresponding to segmented altitude levels. The provider aims to select a subset of the road network to form an air-network with the goal of providing safe and cost effective service for UAV traffic. The air-network selected must provide routes that respect UAV technology restrictions, and must have adequate capacity to support the expected flight volume. We develop a 3D airspace network design model that selects a subset of roads whose 3D projection into the sky will be used for routing flights. The constrained system optimum (CSO) traffic assignment model is used to evaluate the quality of the network; the CSO user constraints represent battery restrictions while minimizing the total travel time ensures realistic routing in the face of congestion. To incorporate the 3D nature of flights, we use simulation to calibrate a Bureau of Public Roads capacity parameter that reflects the multiple vertical layers of airspace made available when a road is selected for the network. We introduce a methodology to derive candidate maps for urban areas and use it on open-source data to build a case study for Chicago city center. We assess the impact of budget, congestion, minimum-path deviation, and demand patterns on network designs.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".