Exploring the Impact of COVID-19 on Aircraft Boarding Strategies Using Discrete Event Simulation
Bibliographic record
Abstract
Aviation is one of the most severely impacted industries by COVID-19.The passenger boarding-process is not only a bottleneck but is also one of the riskiest processes for COVID-19 transmission.There is a need for a decision-support tool that can proactively test the impact of COVID-19 policies on the passenger boarding-process.We achieve this by developing an adaptable modeling approach to Discrete Event Simulation (DES) that simulates the process of boarding under different COVID-19 policies and boarding-strategies.DES model was created using time and motion studies, flightlogs and manuals.Programing-logic was created using n=29 subject-matter experts.As a demonstratorcase, we tested seven of the most common boarding-strategies under different COVID-19 stages: pre-COVID, COVID-19 stage 1 and 2. Preliminary-results show the COVID-19 transmission risk may be decreased with a trade-off: passenger-satisfaction may decrease due to an increase in boarding-time and waiting-time.Steffen's method was most-effective in minimizing COVID-19 risk but is the most difficult to implement.Reverse pyramid and Window Middle Aisle, while slightly less effective than Steffen's method, but overall, more-effective and easier to implement with minimal COVID-19 risk.For COVID-19 stage 1 and 2, boarding time increased up to 33% and 64%, respectively, in-comparison to baseline pre-pandemic conditions.Further, up to 1.5 and 6.6 seat and aisle interferences along with a jetway-seat time of up to 13 minutes were observed.The developed modeling approach serves as a direct response to ICAO's (International Civil Aviation Organization) need for a tool to proactively test and develop policies that minimize COVID-19 risk.
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How this classification was reachedexpand
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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".