Evacuation Simulation Implemented by ABM-BIM of Unity in Students’ Dormitory Based on Delay Time
Bibliographic record
Abstract
China’s university dormitories have high population densities, which can result in a large number of casualties because of crowding and stampedes during emergency evacuations. It is therefore important to plan properly for evacuations by mitigating the effect of choke points that create backlogs ahead of time. Accurate computer representations of the structure of a building and behavior of the evacuees are two important factors to obtain accurate evacuation time. In this paper, Agent-Based Modeling (ABM) and Building Information Modeling (BIM) are, respectively, implemented using the Unity platform to simulate the evacuation process. As a case study, the layout of a student dormitory building at Shanghai Normal University Xuhui District, Shanghai, China, is utilized along with the A* algorithm in Unity to explore the impact of evacuation speed and delays in creating choke points. Compared with previous research, the innovation of this study lies in: (1) using Unity software to make simulation of the physical environment both realistic and easy to implement, demonstrating Unity can be a well-developed platform to implement ABM-BIM research that focuses on crowd evacuation. (2) Using these simulations to evaluate different degrees of congestion caused by varying evacuation speeds, thus providing information about possible issues relating to evacuation efforts. Using the results, several recommended measures can be generated to help improve 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| 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".