Dynamic Sign Guidance Optimization for Crowd Evacuation considering Flow Equilibrium
Bibliographic record
Abstract
The dynamic exit sign has been verified as an effective means to guide the pedestrian during evacuation. The most critical mechanism with dynamic exit sign guidance is to balance the pedestrian flow on each exit route by optimizing the direction of signs. This paper formulates a bi-level programming model for the direction optimization problem of dynamic signs in buildings. In the bi-level program, the upper-level model is a system optimal model, aiming to minimize the total travel time by optimizing the dynamic sign direction. The lower-level model is a pedestrian assignment model satisfying the dynamic user optimal principle that describes the evacuee exit/route choice behaviour to achieve a balanced pedestrian distribution on the route. A method based on the fundamental diagram, the cell transmission model, and the point-queuing theory is developed to estimate evacuation travel time considering congestion and queuing. A heuristic algorithm is extended to solve the bi-level program. Finally, the proposed methodology is validated with numerical examples. Results reveal that the proposed model can produce the optimal dynamic sign direction, significantly improving the evacuation efficiency.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".