A simulation as a service methodology with application for crowd modeling, simulation and visualization
Bibliographic record
Abstract
Crowd modeling and simulation (M&S) has been used to support the analysis of the behavior of crowds, in order to predict the impact of pedestrian movement and to test design alternatives. In recent years, crowd M&S has become more complex, and new technologies such as CAD (computer-aided design) and BIM (building information modeling) authoring tools are being used to support the process. There are challenges in adopting these technologies due to the lack of automation and integration of these tools for crowd M&S. We propose a method based on a distributed architecture with simulation in the cloud, and composition using workflows. In particular, we adopt a model-driven engineering approach to extract data from CAD/BIM authoring tools, Cell-DEVS theory for crowd modeling, simulation as a service to execute simulation remotely, and three-dimensional visualization. Finally, we present a case study for crowd evacuation, discussing the advantages of the proposed architecture. We show the advantages obtained when using distributed deployment, simulation-based design and collaborative development and we discuss how this facilitates the crowd behavior study and improves reusability in crowd M&S.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".