Airport evacuation under panic conditions: a microsimulation modeling applied at Ottawa International Airport
Why this work is in the frame
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Bibliographic record
Abstract
This study develops a framework of pedestrian evacuation microsimulation modeling that considers pedestrians’ social-physiological behavior in assessing an airport evacuation. The study implements social force model within a simulation platform enabling the articulation of stochastic pedestrian walking behavior realistically and reliably. It performs a sensitivity analysis of pedestrian behavior parameters to identify the candidate parameters required to capture pedestrian behavior under different levels of panic conditions. The study considers the case study of the Ottawa International Airport and tests and evaluates contrasting evacuation scenarios under low panic, medium panic, and high panic situations. Results indicate that under the low panic evacuation scenario, the pedestrians yield their movements with an increase in network bottleneck, potentially exhibit cooperative behavior, and control their speed with the rise of crowd density. On the contrary, individuals in high panic evacuation scenarios exhibit aggressive behavior indicated by their average speed, which is approximately 1.15 and 3.5 times the average compared with medium panic and low panic evacuation scenarios, respectively. Results suggest that it takes 5.38 min to evacuate 1300 passengers under high panic conditions compared with 9.75 min for a low panic evacuation scenario. However, in the case of a high panic evacuation scenario, the average speed keeps increasing even with the increase in crowd density. This framework can develop and evaluate strategies for safely evacuating the airport in the case of an emergency.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it