Airport evacuation under panic conditions: a microsimulation modeling applied at Ottawa International Airport
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".