Evaluating Third-Party Impacts in Urban Air Mobility Community Integration: A Digital Twin Approach
Bibliographic record
Abstract
Future Urban Air Mobility (UAM) operations present unique impacts on communities since UAM vehicles will operate primarily in populated areas. This shift from traditional aviation that typically operates point-to-point between urban areas, means that the impacts on third parties are increasingly more important. Safety, privacy, and nuisances such as noise all have third-party outcomes that should be explored to properly design a UAM transportation system that works for users and non-users alike. This thesis explores the use of a Digital Twin to minimize third-party safety and privacy impacts and shows that with accurate live population or mobility data, EVTOL vehicle flight planning is possible that considers where people currently are and adjusts the approach flight path accordingly. A Digital Twin Prototype was developed to represent population density and live traffic data as an equivalent agent-based simulation of the physical world, which becomes the basis for assessing safety and privacy. A case study covering a portion of downtown Montreal is performed for multiple pedestrian and vehicle traffic settings to compare an EVTOL baseline vertiport approach suggested by the Federal Aviation Administration to 127 alternate vertiport approach scenarios to explore generalizations and demonstrate Digital Twin technology for the optimization of UAM operations. It was found that flight path guidelines provided by aviation regulators will not necessarily provide optimal flight paths over urban areas when considering third-party impacts, and it was shown that Digital Twin technology is promising and may play a significant role in promoting community safety and privacy for UAM operations.
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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 | 0.009 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".