Centralized and distributed optimization of advanced air mobility strategic traffic management
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it