A smart spontaneous crowd evacuation system for large multi-exit exhibition centers based on IoT
Bibliographic record
Abstract
Plans to mass evacuate visitors in an exhibition center in the case of emergency situations are critical for public safety and disaster management. Efficient crowd evacuation during mass gatherings has been an active research area during the past years. In this paper, we consider the challenging problem of finding in near real-time the most efficient and safest evacuation pathways in a multi-exit exhibition center while the fire hazard spreads. We first propose a system composed of sensor nodes to collect pertinent safety data associated with the changing environmental conditions. We then present a spontaneous dynamic evacuation system that considers the changing conditions in the risks associated with each hallway segment in terms of walking distance, heat, two major asphyxiant fire gases and crowd congestion. Our IoT-based system activates smart panels placed at major junctions of the hallways to visually guide evacuees towards the safest escape direction under the existing circumstances. The proposed algorithms aim to minimize the total evacuation time of all evacuees, while circumventing congested and perilous aisles, balancing traffic loads, and guaranteeing high scalability and reasonable computational efficiency. This work can pave the way towards the development of nextgeneration smart exhibition centers, where crowd safety is among the top priorities
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".