Modeling Human Behavior for Evacuation Planning: A System Dynamics Approach
Bibliographic record
Abstract
Understanding human behavior during disaster, especially in response to evacuation warning, is very vital and makes a significant difference in reducing potential loss of life. Information on human response to disaster is used in formulating disaster management policies. The engineering aspects of disaster management process are better understood then the behavioral aspects. Proper understanding of human behavior in response to a disaster and our ability to capture it in a dynamic model can be of assistance in disaster management policy analysis. This paper presents a model that simulates the evacuation process during emergency including the mental decision process that leads towards evacuation decisions at the family level. In the model design, determining factors of inhabitant's decision for evacuation during emergency are categorized into four groups: initial factors, social factors, external factors and psychological factors. Dynamic interactions among different factors are captured using a feedback based modeling paradigm called system dynamics. The model developed in this research provides a platform for evaluation of various policy alternatives for emergency evacuation. The model is implemented for the simulation of inhabitant's evacuation behavior during the 1997 flood disaster in the Red River basin in Manitoba, Canada.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".