Pilots’ willingness to operate in urban air mobility integrated airspace: a moderated mediation analysis
Bibliographic record
Abstract
Interest in advanced air mobility (AAM) and urban air mobility (UAM) operations for on-demand passenger and cargo transport continues to grow. There is ongoing research on market demand and forecast, community acceptance, privacy, and security. There is also ongoing research by National Aeronautics and Space Administration , Federal Aviation Administration, academia, and industry on airspace integration, regulatory, process, and procedural challenges. Safe integration of UAM and AAM will also require different stakeholder perspectives such as air traffic controllers, manned aircraft pilots, remote pilots, UAM operators, and the community. This research aimed to assess the willingness of manned aircraft pilots to operate in UAM integrated airspace based on airspace complexity and UAM automation level. In addition, a moderated mediation analysis was conducted using trust and perceived risk as mediators and operator type as a moderating variable. The results indicated that automation level influenced pilots’ willingness to operate an aircraft in integrated airspace. A moderating effect of operation type on automation level and willingness to pilot an aircraft was also observed: professional pilots were more amenable to UAM operations with a pilot on board compared with remotely piloted operations. Results from the study are expected to inform airspace integration challenges, processes, and procedures for UAM integrated 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 | 0.016 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.004 |
| 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".