Probabilistic speed-density relationship for pedestrians based on data driven space and time representation
Bibliographic record
Abstract
This paper proposes a mathematical framework that provides the detailed characterization of the pedestrian flow. It is specifically designed to address the heterogeneity of pedestrian population which is to be reflected through the pedestrian flow indicators. The key components of the presented work are: (i) data driven space discretization framework based on the Voronoi tessellations that allow pedestrian-oriented definition of density indicator; (ii) statistical and data driven approach to time aggregation, allowing for the pedestrian oriented definition of speed indicator; (iii) probabilistic model for speed-density relationship, so as to capture the empirically observed heterogeneity among pedestrians. The estimation and validation of the proposed model are performed on the basis of a pedestrian tracking input. Data is collected in a Lausanne railway station where a large-scale network of cameras has been installed to automatically locate and track thousands of pedestrians. Additionally, the performance provided by this methodology is compared with the well-accepted models published in the literature against empirical data with the aim at improving research on the pedestrian flow characterization.
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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 | 0.001 | 0.001 |
| 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".