A Proposal of the Route Choice Model with Pedestrian’s Individual Map Recognition in a Fire Evacuation
Bibliographic record
Abstract
Evacuation planning is important to mitigate the ill effects of a disaster, such as a fire in earthquakes. For the evacuation of pedestrians, the route choice should maximize the completion rate of the evacuation. Some models of route choice have assumed that pedestrians will recognize the road conditions and the shortest route to the refuge perfectly. However, the validity of the assumption is controversial. In this paper, we propose a new model of route choice, which considers the differences in map recognition between individual pedestrians: the position of the refuge, the cognition of the road and other factors. Then, we discuss an evacuation of pedestrians from a fire, based on the model, including changing the pedestrian’s recognition of the factors. We also utilize a microscopic pedestrian model for simulating the behavior of the pedestrian in the continuum space, based on its visibility. For example, the recognition of the route speeds up the evacuation and raises the completion rate of the evacuation, however, the effect is slight. In contrast, the pedestrian’s recognition of the refuge position more significantly affects the completion rate of the evacuation. These results imply that even rough guidance, such as giving pedestrians the direction of the destination, could increase the completion rate of evacuations significantly.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".