Integrated travel demand models for evacuations: a bridge between social science and engineering
Bibliographic record
Abstract
Since 9/11, the Indian Ocean tsunami and hurricane Katrina, the number of papers that are being published related to mobility simulation in evacuation conditions has signifi cantly increased. Though several topics have been developed, they tend to be implemented with an isolated and non-system approach and for specifi c kinds of dangerous events. This work aims to present a classifi cation and specifi cation of demand models for mobility simulation in evacuation conditions under different evacu-ation scenarios, in respect to different temporal conditions. A general framework is proposed to support the analysis of dangerous events, in respect of type and effects, especially in time. Three different temporal evolutions are identifi ed and systematized: event developments and the relative conditioning on the system; user modifi cation of behavior; and planning and management evolution. Leaving from the integrated temporal evolutions, the user behavior in the system context is analyzed and specifi c models are developed. The importance of SP surveys to analyze user behavior in evacuation conditions is highlighted and a hybrid class of surveys, termed SP with a physical check, is introduced. An inte-grated demand model is specifi ed and calibrated for a dangerous event with effects on travel demand, with diffuse effects in space and delayed in time, according a behavioral approach.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".