On density–flow relationships during crowd evacuation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Traffic and pedestrian dynamics communities often use a standard qualitative classification, namely, level of service (LoS), to describe the relationship between the crowd flow and crowd density in an environment. However, this classification has not yet been rigorously studied in the application of synthetic crowds, which are derived using a variety of approaches and may model certain behaviors better than others. Although synthetic crowds can be simulated to extrapolate crowd flow for rigorous quantitative analysis, these may be at odds with the qualitative LoS. In order to successfully use computer‐assisted design, it is important to have sound quantitative metrics as the basis for analysis and optimization. In this paper, we present a systematic empirical analysis of LoS for synthetic crowds. Using established crowd simulation techniques, we quantify the relation between crowd density and crowd flow for evacuation scenarios across different simulators to explore conformity to qualitative LoS classifications. Following this study, we perform environment optimization experiments under various LoS conditions. Finally, we test the generality of optimizing under these LoS conditions. Our results motivate the need for further study, using real and synthetic crowd datasets across representative environment benchmarks.
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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 | 0.001 | 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 it