Assessment of passengers’ safety and risk attitudes on integrated urban air mobility and airline services
Bibliographic record
Abstract
This study introduces the concept of Air Mobility as a Service (AMaaS) by integrating Urban Air Taxi (UAT) services into the Mobility as a Service (MaaS) framework, aiming to enabling seamless multimodal transportation. The objective is to investigate commuter preferences for adopting multimodal UAT services. A stated choice experiment was designed to capture joint choice of UAT-based alternatives and subscription schemes, alongside attitudinal measures assessing the influence of safety and risk perceptions on adoption behavior. Using data collected in Beijing, a hybrid choice model with latent variables was estimated. Results show that subscription-based schemes, particularly sustainable options like Bike + UAT and PT + UAT, are generally preferred over pay-as-you-go alternatives. Government support and discounts significantly increase adoption likelihood. Safety perceptions also play a critical role. Specifically, perceived UAT safety encourages adoption, while safety consciousness, and perceived UAT risks hinder the use of these services. Individuals with higher safety consciousness are less likely to use pay-as-you-go options, and those perceiving UAT as risky are less inclined to use subscription schemes, particularly Taxi + UAT. These findings provide valuable insights for policymakers and UAT service providers in designing effective policies and marketing strategies to promote UAT adoption.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".