3D Building Evacuation Route Modelling and Visualization
Bibliographic record
Abstract
Abstract. The most common building evacuation approach currently applied is to have evacuation routes planned prior to these emergency events. These routes are usually the shortest and most practical path from each building room to the closest exit. The problem with this approach is that it is not adaptive. It is not responsively configurable relative to the type, intensity, or location of the emergency risk. Moreover, it does not provide any information to the affected persons or to the emergency responders while not allowing for the review of simulated hazard scenarios and alternative evacuation routes. In this paper we address two main tasks. The first is the modelling of the spatial risk caused by a hazardous event leading to choosing the optimal evacuation route for a set of options. The second is to generate a 3D visual representation of the model output. A multicriteria decision making (MCDM) approach is used to model the risk aiming at finding the optimal evacuation route. This is achieved by using the analytical hierarchy process (AHP) on the criteria describing the different alternative evacuation routes. The best route is then chosen to be the alternative with the least cost. The 3D visual representation of the model displays the building, the surrounding environment, the evacuee’s location, the hazard location, the risk areas and the optimal evacuation pathway to the target safety location. The work has been performed using ESRI’s ArcGIS. Using the developed models, the user can input the location of the hazard and the location of the evacuee. The system then determines the optimum evacuation route and displays it in 3D.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".