Centralized and distributed optimization of advanced air mobility strategic traffic management
Bibliographic record
Abstract
Effective traffic management for advanced air mobility (AAM) operations in low-altitude urban airspace is crucial for safety and scalability. Our study aims to bridge a critical gap in AAM traffic management by minimizing travel delays in both centralized and distributed providers of services for urban air mobility (PSU) settings. Key contributions include methods to (1) sectorize urban airspace for effective AAM management, (2) centrally plan AAM routes considering limited capacities in corridors and vertiports, and (3) manage airspace in distributed PSU settings while considering traffic flow capacities and interactions among PSUs. Specifically, the research combines community detection algorithms with Voronoi diagrams to sectorize individual PSU airspace. Corridor route planning is performed with a custom-weighted Dijkstra’s algorithm. Centralized AAM traffic flow management adopts mixed-integer programming (MIP) to minimize overall network delay costs. Distributed PSU network management is formulated as bi-level optimization using cooperative game theory and MIP, where individual PSUs update their strategies based on game theory outcomes. The simulation environment features a randomized no-fly zone, population density maps, and vertiport capacities assigned to artificial cities. Three vehicle configurations with varying ranges and adjustable speeds (i.e., minimum to cruise speeds) are simulated under three service priorities in Monte Carlo simulations. AAM flight operations are evaluated by optimization cost and runtime. This research provides a technical framework and insights into the comparison of centralized and distributed AAM network managements. The paper will facilitate informed decision-making in the development and implementation of AAM traffic management strategies.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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) | 0.001 | 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".