Deboarding simulation · A320, 30 rows 3-3, door L1 · top view to scale
After landing, people already standing in the aisle usually stop to let each row out. The hypothesis: it would be faster if the aisle never stopped and those still seated waited for a real gap, using the wait to get their things ready. Both planes carry exactly the same passengers; only that rule changes.
Changing any of them regenerates both planes with the same passengers and reruns the Monte Carlo.
Cumulative over time, for this particular plane.
One plane can go well or badly by chance. Here 40 different planes are simulated with the parameters above, under both rules and intermediate degrees of courtesy. Differences are paired: the same passengers are compared.
Mean over all planes, with a 95% confidence interval.
| Probability of yielding | Mean total time | 95% CI | Mean exit per passenger | Mean rate |
|---|
Sweep with 100 planes per value (node sweep.js, the same model as this page and the Python simulator): one parameter changes at a time and the rest keep their defaults. The last column is how much longer courtesy takes than aisle first.
| Parameter | Value | Courtesy | Aisle first | Difference (95% CI) |
|---|
Every number is a parameter with its source; those without data yet are estimates waiting for video measurements (see DATA.md).