Using synthetic crowds to inform building pillar placements
Bibliographic record
Abstract
We present a preliminary exploration of synthetic crowds towards computational tools for informing the design of environments (e.g., building floor plans). Feedback and automatic design processes are developed from exploring crowd behaviours and metrics derived from simulations of environments in density stressed scenarios, such as evacuations. Computational approaches for crowd analysis and environment design benefit from measures characterizing the relationships between environments and crowd flow behaviours. We investigate the optimization of environment elements to maximize crowd flow, under a range of LoS conditions, a standard indicator for characterizing the service afforded by environments to crowds of specific densities widely used in crowd management and urban design. The steering algorithm, the number of optimized environment elements, the scenario configuration and the LoS conditions affect the optimal configuration of environment elements. From the insights gained exploring optimizations under LoS conditions, we take steps towards user-in-the-loop optimization and design of an environment by applying an adaptive refinement approach to reduce the search space of the optimization. We derive the fitness of architectural layouts from background simulations. We perform a ground truth study to gauge the performance and quality of our method.
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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 | 0.000 | 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".